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Green warehousing, logistics
optimization, social values and
ethics and economic performance:
the role of supply
chain sustainability
Yaw Agyabeng-Mensah
Transportation Engineering College, Dalian Maritime University, Dalian, China
Esther Ahenkorah
The role of
supply chain
sustainability
Received 19 October 2019
Revised 25 January 2020
14 March 2020
21 April 2020
14 June 2020
Accepted 30 June 2020
Regent University College of Science and Technology, Accra, Ghana
Ebenezer Afum
Transportation Engineering College, Dalian Maritime University, Dalian, China
Essel Dacosta
Dalian Maritime University, Dalian, China, and
Zhongxing Tian
Transportation Engineering College, Dalian Maritime University, Dalian, China
Abstract
Purpose – This study primarily explores the influence of green warehousing, logistics optimization and social
values and ethics on supply chain sustainability and economic performance. The study further examines the
mediating role of supply chain sustainability between economic performance and green warehousing, logistics
optimization and social values and ethics.
Design/methodology/approach – The study employs a quantitative research approach where survey data
are collected from 200 managers of manufacturing companies in Ghana. The dataset is analyzed using partial
least square structural equation modeling software (PLS-SEM) SmartPLS 3.
Findings – The results show that green warehousing and logistics optimization negatively influence economic
performance but improves economic performance through supply chain sustainability. It is further discovered
that social values and ethics have a positive influence on supply chain sustainability and economic
performance.
Originality/value – This paper proposes and tests a theoretical model that explores the relationships between
green warehousing, supply chain sustainability, economic performance, logistics optimization and social
values and ethics through the resource dependency theory (RDT) in the manufacturing firms in Ghana.
Keywords Supply chain sustainability, Green warehousing, Logistics optimization, Social values and ethics,
Performance, Resource dependency theory
Paper type Research paper
1. Introduction
Supply chain activities of firms have led to environmental pollution, global warming and
climate change and termination of human lives through the emission of greenhouse gases,
improper waste management, production of non-biodegradable products, the inappropriate
keeping of hazardous and explosive substances and excessive use of resources (Khan, 2019;
Dekker, 2012; Feng et al., 2018; Khan et al., 2018). Firms are adopting green and social
practices into their supply chain to reduce the impact of their activities on the environment,
ensure societal safety, improve efficiency, gain competitive advantage, meet stakeholders’
demand and access new markets (Agyabeng-Mensah et al., 2020a, b, c, d). These socially and
The International Journal of
Logistics Management
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-10-2019-0275
IJLM
environmentally inclined practices are adopted to ensure the safety and welfare of employees
and other needs of stakeholders through less emission of greenhouses gases, waste
reduction and resource and energy consumption (Longoni et al., 2018; Khan et al., 2018; Zaid
et al., 2018). Nonetheless, the implementation of these socially and environmentally friendly
practices face challenges due to their confusing and inconsistent influence on sustainability
performance (Zaid et al., 2018; Adomako et al., 2019; Green et al., 2019a, b; AgyabengMensah et al., 2020a, b, c, d). Many studies have been conducted into the relationship
between green practices and environmental conservation (Nilsson, 2013; Gotschol et al.,
2014), continuous improvement (Carrizo-Moreira, 2014), financial performance (Liu et al.,
2012; Sarkis et al., 2011) and social performance (Zaid et al., 2018; Agyabeng-Mensah et al.,
2020a, b, c, d). Agyabeng-Mensah et al. (2020a, b, c, d) and Baah et al. (2020) found a positive
relationship between green logistics practices and financial performance. Green et al.
(2019a, b) suggest that green and lean practices combine to advance environmental
sustainability among manufacturing firms in the US. Zaid et al. (2018) established a positive
relationship between green practices and sustainability performance among Palestinian
manufacturing firms. Longoni et al. (2018) discovered that green practices improve
financial performance among firms in Italy, while Feng et al. (2018), which was conducted in
the Chinese automobile manufacturing companies, found that green supply chain practices
harm sales, profit margin and return on assets. Despite the extensive studies into green
practices, scholars continue to root for more studies into the potency of green practices in
the achievement of supply chain sustainability. Dubey et al. (2017a, b) and Tseng et al.
(2019) further established that there is relatively less sustainability literature from the
emerging economy perspective especially, in Africa. Besides, Eriksson et al. (2015) urge
future studies to look into the role of social values and ethics and moral responsibility in
supply chain sustainability.
Moreover, Bartolini et al. (2019) suggest that there is inadequate literature, which
considers green warehousing as a standalone variable. The aforementioned gaps have
motivated us to conduct this study to advance the course of sustainability literature and
practice. The study uses the resource dependency theory (RDT) to explore the influence of
logistics optimization, green warehousing, social values and ethics on supply chain
sustainability and economic performance.
Further, the paper explores the indirect influence of logistics optimization, social values
and ethics and green warehousing on economic performance through supply chain
sustainability. This leads us to answer the research questions;
(1) Do logistics optimization, green warehousing and social values and ethics directly
influence supply chain sustainability and economic performance?
(2) Does supply chain sustainability mediate the relationship between logistics
optimization, social values and ethics, green warehousing and economic
performance?
The study will deepen the understanding of green supply chain practices from the developing
economy’s perspective to strike a balance between literature since this study responds to the
call from the social and green supply chain management literature (Eriksson et al., 2015;
Dubey et al., 2017a, b; Tseng et al., 2019) to conduct empirical studies into the constructs used
in this research. The study’s empirical model will serve as a blueprint for future study since it
is among the few, if not the first empirical study that tests the link between green
warehousing, social values and ethics, logistics optimization, supply chain sustainability and
economic performance. Besides, it will provide evidence for practitioners to push for green
supply chain practices as a strategy for achieving sustainability objectives. The first part of
the study contains the introduction. The literature review forms the second part while
research design, data analysis, results and discussion and conclusion form the third, fourth
and fifth sections, respectively.
2. Literature review
2.1 Resource dependency theory (RDT)
RDT is one of the strategic theories that received attention through the work of Salancik and
Pfeffer (1978). RDT was not originally developed as a profit seeking theory. However, the
theory has been developed to include profit seeking objective which is congruent with
organizational economics-based approaches and is capable of explicating the differences
between performances (e.g. Pfeffer, 2005; Villalonga and McGahan, 2005). RDT posits that
naturally, firms do not have adequate resource to implement their strategies and plans
individually to achieve expected performance outcome. Hence it is imperative for them to
partner with other supply chain members to obtain adequate and needed resource (Pfeffer
and Salancik, 2003) for sustainable development (Ulrich and Barney, 1984). The RDT
suggests that there is the need for interdependence among firms in order to obtain sufficient
resource to sustain growth among themselves (Salancik and Pfeffer, 1978). This reflects the
postion of the RDT, which indicates that firms cannot claim full self-sufficiency regarding
critical resources. The RDT proposes a collaboration among firms in a supply chain in order
to achieve higher performance gains. Green warehousing, logistics optimization and social
values and ethics are typical resources needing the collective efforts of supply chain partners
for effective implementation to improve performance benefits (Shang et al., 2010; Zhu et al.,
2005). According to Sarkis et al. (2011), the role of the quality and effectiveness of supply chain
collaboration in the implementation of sustainable/green supply chain management practices
should not be undermined. Collaboration with supply chain partners can be properly
managed and developed into relationship-specific assets to serve as sources of competitive
advantage. The RDT calls for proper relationship between a focal firm and its suppliers and
customers to reduce the environmental uncertainty around the operations of the firm
(Carter and Rogers, 2008; Cao et al., 2010). Zhu et al. (2005) suggest that collaboration among
supply chain partners is essential for internal and external coordinations for effective
implementation of sustainable supply chain practices (green warehousing, social values and
ethics and logistics optimization) to achieve expected performance results. The RDT posits
that firms that have power within the supply can advance the course of spreading sustainable
supply chain management practices among supply chain members. Gonzalez et al. (2008)
established that larger firms with superior powers in the supply chain will expect their small
supplier firms to adopt socially, ethically and environmentally sound practices to achieve
sustainable development goals. This study adopts the RDT to explicate the role of adopting
sustainable/green supply chain management practices such as green warehousing, logistics
optimization and social values and ethics in performance improvement among manufacturing
firms in Ghana.
2.2 Development of hypotheses
2.2.1 Green warehousing and supply chain sustainability. Warehousing is one of the critical
activities of in-house and outbound logistics and distribution. According to McKinnon (2010),
warehousing and goods handling contribute to 2–3 percent of energy-related CO2 emissions
worldwide. Abushaikha (2018) and Gu et al. (2016) suggest that warehouses may serve as a
source of non-value added activities due to the numerous activities associated with its
operation. Warehousing activities produce enormous waste in the supply chain, which
require the adoption of practices and policies that reduce waste to eliminate their adverse
impact on the environment and human lives (Abushaikha, 2018). Again, warehousing
activities especially, where hazardous substances are kept pose several risks to the safety of
The role of
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the environment and health of the employees in situations where employees’ knowledge is
limited and proper labeling practices are not adopted. Warehousing activities such as
movement of vehicles from one warehouse to the other also increase the carbon dioxide
emission in the environment. This shows that warehousing is an area of logistics that requires
serious attention from firms undertaking supply chain sustainability projects to meet the
requirements of stakeholders and gain competitive advantage (Abushaikha, 2018). Several
scholars (Yildiz Çankaya and Sezen, 2019; Colicchia et al., 2011) have realized the importance
of warehouse sustainability and suggested the usage of green energy sources and strategies
as well as the adoption of energy-efficient handling technologies to manage the warehouse
and its related issues. According to Tan et al. (2009), firms should adopt information
technology such as iThink, which is useful for forming dynamic connections among social,
environmental and economic issues. Green packaging which includes using green packaging
materials, cooperating with sellers to ensure standardization of packaging, reducing material
usage and unpacking time, adopting returnable packaging methods and promoting recycling
and reuse programs (Ninlawan et al., 2010) improves sustainability in the warehouse. The use
of recyclable and biodegradable materials for repackaging of products in the warehouses
may ensure environmental sustainability. Besides, green packaging may reduce the use of
materials and optimize the use of space within the warehouse to ensure efficiency. According
to Harris et al. (2014), firms that aim at improving supply chain sustainability and economic
performance may adopt proper warehouse management system. Space maximization and
just-in-time (JIT) are essential to green warehousing (Tan et al., 2009). JIT aims at reducing
inventory costs and waste using less storage and warehouse space (Green et al., 2019a, b).
Moveover, according to Agyabeng-Mensah et al. (2020a, b, c, d), JIT reduces overhead cost
and consumption of resources through proper inventory management. Wu and Dunn (1995)
suggest that proper use of storage area, reduction of retrieval cost and energy usage are vital
goals of green warehousing. Wang et al. (2015) highlight the relevance of recycling facilities in
promoting green warehousing to advance supply chain sustainability. Green warehousing
requires the collaborative effort of supply chain members in order to achieve the expected
sustainability performance outcome. The effort of suppliers is required in ensuring that green
materials are provided to promote green warehousing activities. Hence, from the RDT
perspective, we hypothesize that;
H1. Green warehousing has a significant positive influence on supply chain
sustainability.
2.2.2 Logistics optimization and supply chain sustainability. Logistics activities form a
significant part of supply chain activities. Generally, the nature of logistics activities makes
them environmentally unfriendly. Logistics activities such as transportation contribute
hugely to environmental pollution and the greenhouse effect (Khan, 2019). Khan et al. (2007)
argued that freight transport contributes about 8% of the world’s energy-related CO2
emissions. In the road transport sector, the amount of energy used to move freight is
increasing at a faster rate due to high demand for goods and services, which has resulted in
the emission of greenhouse gases that are harmful to human health and pollute the
environment (Hishan et al., 2019). This is an indication that logistics activities have
undesirable effect on the environment and human lives. There is a need to optimize logistics
activities to reduce adverse environmental impact and ensure environmental sustainability
(Agyabeng-Mensah et al., 2020a, b, c, d). Logistics optimization involves the implementation
of reverse logistics practice, collaboration and the use of clean fuel to considerably control
environmental effect of the operations of an organization (Neto et al., 2008; Boix et al., 2015). In
other words, logistics optimization involves the adoption of practices that green the supply
chain operations to reduce externalities and enhance profitability. According to Halldorsson
and Kovacs (2010), there is the need to develop and implement energy-efficient logistics
practices and supply chain systems to reduce global carbon footprint through the adoption of
clean fuel and proper routing systems. Firms have to optimize their routes and use clean fuels
to ensure the reduction of pollution that harms human lives (Khan, 2019). Tang et al. (2016)
argue that firms must locate their warehouses closer to consumers to reduce the long
traveling distances, which leads to the reduction of carbon emissions.
Further, Tang et al. (2016) suggest that companies may build their warehouses in places
that ensure the use of alternative ecofriendly transportation modes to advance supply
chain sustainability. According to Dowlatshahi (2000) and Gonzalez-Torre et al. (2004), the
development of reverse logistics channels decreases the usage of resources, and improves
recycling and reuse of products leading to improvement in the achievement of supply chain
sustainability objectives. Reverse logistics is a concept that involves reclaiming, inspecting/
selecting/sorting, re-processing/direct recovering and redistributing products to retrieve value
(Hansen et al., 2018). Firms, which employ reverse logistics, reduce the impact of their products
on the environment while ensuring that several people are hired into the supply chain (Baah,
2019). Logistics collaboration involves bringing together supply chain partners to set
congruent goals and map strategies to achieve the strategies through information and resource
sharing. Agyabeng-Mensah et al.(2020a, b, c, d) found that green logistics practices have
positive influence on environmental and social performances. Firms may be required to share
resources and skills with supply chain partners to implement green policies to achieve
environmental and social sustainability goals. Based on the literature review, it could be
established that collaborative efforts from suppliers and customers are essential for proper
implementation of logistics optimization to significantly advance supply chain sustainability.
Thus, through the RDT, we hypothesize that;
H2. Logistics optimization has a significant positive influence on supply chain
sustainability.
2.2.3 Social values and ethics and supply chain sustainability. Dubey et al. (2017a, b) posit that in
recent times, scholars have given considerable attention to the role of social values and ethics in
sustainable development and have hugely debated the topic. Ethical theories are founded on
ideologies that demonstrate what is right or the need to build a good society” (Garriga and Mele,
2004, p. 60). Social values and ethics comprise the practice of engaging in morally and socially
acceptable activities to enhance supply chain sustainability. According to Gunasekaran and
Spalanzani (2012), the successful implementation of social value and ethics ensures the welfare
and safety of community members and employees. Cantor et al. (2012) argue that corporate
sustainability programs require the management of firms to motivate employees by getting
them involved in the business processes to comprehend the prime matters of the firm and
embrace the newly carved visions of the firm. Scholars such as Drake and Schlachter (2008) and
Muller (2009) argue that ethical sourcing and purchasing lead to improved environmental
performance. Ethical purchasing involves the buying of harmless goods and inputs from
suppliers who are ethically sound. Ethical purchasing may ensure that the best practices in
sourcing are obeyed to safeguard the environment and human lives through the selection and
buying of new products and raw materials from suppliers whose activities are not detrimental
to human lives and the environment. According to Beamon (2005), engineering ethics
significantly contributes to the design and development of an ecofriendly supply chain. Chiou
et al. (2011) argue that green purchasing and eco-innovation improve green competitiveness
and performance. Studies indicate that firms can improve their environmental performance
when they collaborate on adopting green practices that offer win-win opportunities with their
stakeholders (customers) (Zhu et al., 2017). Thus, through the RDT, we hypothesize that;
H3. Social values and ethics have a significant positive influence on supply chain
sustainability.
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2.2.4 Logistics optimization and economic performance. Performance is the measure of the
outcome of the combination of an organization’s process, strategies and resources to achieve
organizational goals. Performance improvement is key to the survival of every firm
(Agyabeng-Mensah et al., 2019a, b, c). Performances are measured to know how effectively
they have employed shareholders’ funds entrusted into their care for the benefit of all
stakeholders. Performance of a firm has been measured from several perspectives. According
to Agyabeng-Mensah et al. (2019a, b, c) performance involves financial and non-financial
outcomes of application of business processes, activities, policies, capabilities and resources.
Several variables contribute to the performance of firms, especially economic performance
(Baah and Jin, 2019), which is applied in this study. Economic performance is the outcome of
the combination of resources, policies and capabilities of firms to achieve operational,
marketing and financial performances through the fusion of social and green practices into
the firms’ supply chain operations. Logistics activities contribute significantly to the cost of
operation, which may negatively affect economic performance (Agyabeng-Mensah et al.,
2020a, b, c, d). Fuel cost, transportation cost, legal fees relating to the violation of regulations,
buying of carbon emission cards, waste of resources and excessive use of fuel contribute to
the enormous logistics cost that has a massive impact on economic performance (Simper et al.,
2019; Agyabeng-Mensah et al., 2020a, b, c, d). However, logistics activities could serve as
economic performance booster when they are effectively managed (Agyabeng-Mensah et al.,
2020a, b, c, d). There is a need for firms to ensure that logistics optimization enhances logistics
efficiency and improves economic performance (Niknejad and Petrovic, 2014; Garetti and
Taisch, 2012). Organizations may collaborate with supply chain members to undertake risky
and costly green and social projects that may project the image of the firm to attract new
customers to enhance economic performance (Agyabeng-Mensah et al., 2020a, b, c, d). Zaid
et al. (2018) suggest that a firm’s collaboration with its supply chain partners may enhance
economic performance. Besides, logistics optimization activities such as reverse logistics
practices help firms to meet the environmental regulatory requirements and stakeholder
pressures, demanding firms to save money through the elimination of costs associated with
fines and legal battles (Agyabeng-Mensah et al., 2020a, b, c, d; Baah, 2019). Stakeholders are
ready to pay more for the goods of companies that are ecofriendly, leading to improved
profitability (Green et al., 2019a, b). Thus, we develop the hypothesis,
H4. Logistics optimization has a significant positive influence on economic performance.
2.2.5 Green warehousing and economic performance. Several firms carry out green activities
with the aim of improving the economic performance of firms and reducing environmental
impact (Feng et al., 2018; Baah and Jin, 2019). According to Coyle et al. (2013) and Amemba
et al. (2013), green warehousing involves using minimum energy and maximizing space usage
to reduce cost and ensure efficiency to enhance economic performance. Besides, green
warehousing involves the use of pollution-free energy, which may save firms the cost of
environmental fines. Moreover, warehouses built with high-energy performance certificates
consume less energy to reduce energy cost and advance profitability (Indrawati et al., 2018).
Further, green warehousing may eliminate the cost of pollution control through zero
production of waste and emission, which may translate into improved economic performance.
Again, green activities that deal with lighting, air tightness and thermal insulation in the
warehouses ensure a reduction in energy consumption. According to Cox and Graham (2010),
green warehousing provides optimal use of capacity to increase profitability. Green
warehousing activities like any other green supply chain practices are acknowledged as an
efficient and broad approach to achieving higher operational performance (Wong et al., 2012;
Zailani et al., 2012; Zhu et al., 2008). The use of green technologies in warehouses may result in
less waste management and improve process quality, which creates ability for firms to
respond to changes in customers’ demand leading to improved sales and profitability.
Customers perceive greener products as goods of better quality, which leads to an increase in
the market share of the firm through customer satisfaction and loyalty (Baah, 2019).
Torabizadeh et al. (2020) suggest that the introduction of green practices into supply chain
activities such as warehousing has a positive influence on economic performance.
Ecologically responsible behavior mitigates risk and, subsequently, enhances economic
performance (Zhang and Chen, 2017). We hypothesize that;
H5. Green warehousing has a significant positive influence on economic performance.
2.2.6 Green warehousing, logistics optimization, social values and ethics, supply chain
sustainability and economic performance. Sustainability is a ubiquitous word used across all
the critical functions of firms (Zaid et al., 2018) and among scholars in supply chain. Soytas
et al. (2019) indicate that the adoption of sustainable development principles may necessitate
changes in operations and policies of firms to be able to remove adverse social and
environmental effects. The study defines supply chain sustainability as the measurement of
positive influence of social policies and green practices on energy and resource conservation
and protection of the environment. This means that supply chain sustainability covers
environmental and social performances in this study. According to Feng et al. (2018), green
practices positively influence environmental performance leading to low cost of operation,
low price of goods and increased market size, sales, net profit margin and return on
investment among Chinese automobile manufacturing firms. Longoni et al. (2018) suggest
that green practices improve financial performance through environmental performance.
Makov and Newman (2016) argue that green practices which advance the welfare of
employees and the members of the society enhance the reputation of the firms. This may
attract potential customers and retain existing ones leading to improved economic
performance. Employee safety and welfare could be improved through the adoption of
green logistics practices that reduce greenhouse gas emission and proper management of
waste (Baah, 2019) to reduce health problems (Khan, 2019). Dubey et al. (2017a, b) suggest that
green practices in supply chain enhance green brand image and ensure brand equity
resulting in cost savings and improved customer satisfaction. Logistics optimization, social
values and ethics and green warehousing activities improve financial performance through
cost savings while producing and providing ecofriendly products to satisfy the varying needs
of customers. Torabizadeh et al. (2020) suggest that green warehousing has a positive
influence on firm performance. Again, ensuring the safety and welfare of employees and
external stakeholders through social values and ethics improve social sustainability of firms
which reduces labor turnover and recruitment cost (Longoni et al., 2018). Green supply chain
practices retain competently and attract talented employees that have the requisite skills to
implement environmental practices and social policies to reduce waste, conserve energy and
resources, ensure employee and societal safety and welfare and improve economic
performance (Zaid et al., 2018; Agyabeng-Mensah et al., 2020a, b, c, d). Prior studies
suggest that green practices in the supply chain lead to leanness and efficiency, which
increase market shares and create profitability (Feng et al., 2018; Baah, 2019). Soytas et al.
(2019) provide empirical evidence to confirm the positive relationship (possibly causally)
between corporate social sustainability and financial performance. Besides, AgyabengMensah et al. (2020a, b, c, d) suggest that social and environmental performances influence
financial performance positively. Allouche and Laroche (2005) and Gonenc and Scholtens
(2017) posit that social performance is positively related with financial performance
through the satisfaction of customers’ utility. However, Lu and Abeysekera (2014) suggest
that there are inconclusive findings between financial performance and corporate social
performance. Thus, we examine the relationship between green warehousing, social values
and ethics, supply chain sustainability and economic performance through RDT. Therefore,
we hypothesize that;
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H6. Social values and ethics has a significant positive influence on economic
performance
H7. Supply chain sustainability has a significant positive influence on economic
performance.
H8. Supply chain sustainability mediates the relationship between green warehousing
and economic performance.
H9. Supply chain sustainability plays a mediating role between logistics optimization
and economic performance.
H10. Supply chain sustainability plays a mediating role between social values and ethics
and economic performance.
3. Research design
3.1 Data collection
The respondents of this study were selected from the manufacturing industry in Ghana. The
context of this study is Ghana because of the inadequate literature addressing supply chain
sustainability issues in emerging economies and Africa (Baah, 2019; Zaid et al., 2018; Bastas
and Liyanage, 2019). Four hundred and twenty-three (423) firms were sampled from the five
hundred and seventy (570) manufacturing firms in Ghana from the database of
Manufacturers Association. The firms were contacted through phone calls to assess their
willingness and eligibility to participate in the study. Two hundred and sixty-one (261) firms
were eligible and agreed to participate in the study. Two hundred and sixty-one (261)
questionnaires and confidentiality and assurance letters seeking permission, explaining the
academic purpose of the study and assuring them of our duty to hide their identities were sent
to the respondents through the mail. The respondents were given one month (from June 1 to
30, 2019) to complete the questionnaires. We sent a two-day interval message to remind the
respondents to fill the questionnaires after the first 18 days to increase participation. We
received two hundred and ten (210) questionnaires at the end of June 2019, where ten (10) were
invalid due to missing data. The two hundred (200) valid responses represented 35.08% (200/
570) of the population. The questionnaires were collected in two waves, where the early wave
of response (within the first 18 days) was one hundred and eight (108), and the late wave of
response (within the last 12 days) was ninety-two (92). The respondents included managing
directors, procurement managers, supply chain managers, warehouse managers, logistics
managers who have not less than five years of work experience and have occupied their
current positions for not less than eight years. The profiles of the respondents place them in a
suitable position to provide the necessary and relevant data for this study. The soft drink
manufacturers (44%), textile manufacturers (16.5%), shoe manufacturers (16%) and
furniture manufacturers (12%) were the contributions of each of the industries toward the
data used for this study. The details of the companies and the respondents are contained in
Tables 1 and 2.
3.2 Conceptual model
The conceptual model shows the relationship between the dependent variables (economic
performance), mediating variable (supply chain sustainability) and the independent variables
(green warehousing, logistics optimization and social values and ethics). The directions of the
arrows suggest the flow of influence from logistics optimization, social values and ethics and
green warehousing to supply chain sustainability and economic performance. The first model
looks at the direct effects between green warehousing, logistics optimization and social
Companies
Textiles factory
Soft drinks
Shoe manufacturers
Stationary
Furniture
Totals
Number of firms
Percentage (%)
33
88
32
23
24
200
16.5
44
16
11.5
12
100
1–5
6–10
11–15
16–20
Above 20
Total
Age (Years)
21
32
43
65
37
200
10.50
16
21.50
32.5
18.5
100
Below 10
10–40
40–80
80–120
120–600
Above 600
Total
Annual sales (million Cedis)
29
63
42
27
22
17
200
14.50
31.50
21
13.50
11
8.50
100
Number of employees
42
79
33
18
12
16
200
20
39.5
16.5
9
6
8
100
1–99
100–199
200–299
300–399
400–499
500 and above
Total
Position
Number
Percentage (%) of respondents
Managing directors
Procurement manager
Supply chain manager
Warehouse managers
Logistics managers
Total
Work experience (Years)
Below 5
5–10
10–15
16–20
Above 20
Total
50
15
42
48
45
200
Number
16
73
23
39
49
200
25
7.5
21
24
22.50
100
Percentage
8.00
36.50
11.50
19.50
24.50
100
values and supply chain sustainability. Moreover, the model B shows the direct effect
between green warehousing, logistics optimization, social values and ethics, supply chain
sustainability and economic performance. Further, the model C shows the indirect influence
of green warehousing, logistics optimization and social values and ethics on economic
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Table 1.
Profile of responding
companies
Table 2.
Profile of respondents
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performance through supply chain sustainability. Based on RDT, we propose and test a
model to ascertain how social values and ethics, logistics optimization and green
warehousing could be employed by firms to achieve improved performance (supply chain
sustainability and economic performance) as shown in Figure 1.
3.3 Operationalization of the constructs
In order to test the hypotheses in this study, we use a quantitative research approach where
questionnaires are utilized to gather data. Developing the right measures for constructs is one
of the challenges facing today’s scholars (Agyabeng-Mensah et al., 2020a, b, c, d; Nawanir
et al., 2013; Abushaikha, 2018), which requires a lot of work. We developed the items used to
measure the theoretical constructs through extensive literature review. The data is gathered
from the databases of Web of Science, Scopus and EBSCO. Because our study focuses on
firms that have adopted relevant sustainable supply chain initiatives, we used highly
experienced experts in sustainable supply chain practices from the academia and industry to
develop clear, accurate and reliable scales and for pretesting of our questionnaires. Six
questionnaires were emailed to six experienced supply chain academics and managers to
express their opinions on the reliability, accuracy, validity and clarity of the instrument. The
comments received from the experts caused us to drop and add some measures relevant to
and representative of the study context. In order to achieve high statistical variability among
the responses, the measurement items were measured using five-point Likert scale anchored
from (1) strongly disagree to strongly agree (Chen and Paulraj, 2004; Dubey et al., 2017a, b).
Existing scales were adapted to suit logistics optimization, green warehousing, social values
and ethics, supply chain sustainability and economic performance after a consultation with
experienced academics in sustainable supply chain management. The respondents were
asked to select one of the anchors to rate the items from 1 5 strongly disagree, 2 5 disagree,
Green
warehousing
Supply chain
sustainability
Social values
and ethics
Logistics
optimization
Control variables
Nature of industry
Firm size
Independent effects
(a)
Green
warehousing
Green
warehousing
H5
H1
Social values
and ethics
Economic
performance
Logistics
optimization
Supply chain
sustainability
Independent effects
Social values
and ethics
(b)
Figure 1.
Proposed
conceptual model
Logistics
optimization
Supply chain
sustainability
H7
H6
Green
warehousing
Social values
and ethics
H3
H2
H4
Supply chain
sustainability
Economic
performance
Supply chain sustainability
as a mediator
(c)
Logistics
optimization
Full model
(d)
Economic
performance
3 5 neutral, 4 5 agree and 5 5 strongly agree to show the degree of their disagreement or
agreement. The study used reflective first-order level models to measure green warehousing,
logistics optimization, social values and ethics, supply chain sustainability and economic
performance. Logistics optimization was measured using six items. The items were adapted
from Nikolaou et al. (2013), Vijayan (2014) and Boix et al. (2015). Also, green warehousing was
measured using seven items. The items were adopted from Coyle et al. (2013) and Amemba
et al. (2013). The study employed eight items to measure social values and ethics, which were
adopted from Sarkis et al. (2011), Hoejmose et al. (2013), Gunasekaran and Spalanzani (2012)
and Eriksson et al. (2015). Supply chain sustainability was measured using eight items
adopted from and Green et al. (2019a, b), Zaid et al. (2018) and Longoni et al. (2018). Economic
performance was also measured using seven items adapted from Agyabeng-Mensah et al.,
2020a, b, c, d and Abushaikha (2018). The constructs and their respective measures are shown
in appendix and Figures 1 and 2. Nature of industry and firm size is are likely to have
influence the adoption of green practices (Agyabeng-Mensah et al. (2020a, b, c, d)) and social
values and ethics that may affect supply chain sustainability and economic performance.
Also, certain industries command extensive sustainable practices to regulate the adverse
impact of their operations on the environment and society. On the other hand, large firms
have adequate resource capacity to implement sustainable supply chain practices as
compared to small firms (Liang and Yuyan, 2007). This study employs sales and number of
employees to connote firm size. This study employs firm size and nature of industry as
control variables to curtail the confounding effect of the exogenous constructs on the
endogenous constructs (Chen and Paulraj, 2004).
The role of
supply chain
sustainability
3.4 Nonresponse, common method bias and endogeneity tests
The results of the paper could be affected by nonresponse bias since the data were collected in
two waves using mails and questionnaires. We followed the suggestion of Armstrong and
Overton (1977) to test the nonresponse bias of the early and late responses. The results of our
test for nonresponse bias between the early 108 response and the late 92 response using the
t-test show that nonresponse bias is not a problem in this study since the early and late waves
of responses are not substantially different at the 5% significance level. This is one of the
commonly used methods in the supply chain management literature (Green et al., 2019a, b;
Inman and Green, 2018). Moreover, a t-test conducted to examine the difference in perceptions
of the respondents about the implementation and the influence of green warehousing,
logistics optimization and social values and ethics on supply chain sustainability and
Control variables
Nature of industry
Firm size
Green
warehousing
Social values
and ethics
–0.408**
0.371**
0.358**
0.245**
Economic
performance
0.173*
0.344**
Logistics
optimization
Supply chain
sustainability
–0.213**
Note(s): ** = significance, * = significance. All the paths are significant at 5% confidence interval
Figure 2.
Measurement model
IJLM
economic performance suggest that there is no significant difference in perception among
managing directors, procurement managers, supply chain managers, warehouse managers
and logistics managers.
Podsakoff et al. (2003) posits that standard method bias test deals with an exploratory
factor analysis (EFA) considers all observed variables and when a single factor explains a
value ≥ 0.50 (i.e., ≥50%), which is a majority of the cumulative variance among measures,
then, there is common method bias. The EFA performed on the variables in this study
suggests 0.2722 (27.22%) as the first extracted factor explicated of the variance, which is
below the 50% threshold. Hence, it could be reasonably and sufficiently confirmed that our
study is without common method bias.
As suggested by Guide and Ketokivi (2015), we conducted an endogeneity test of the
exogenous constructs in our proposed model. Green warehousing, logistics optimization and
social values and ethics are conceptualized as constructs antecedent to supply chain
sustainability an economic performance. This was done with the consideration that green
warehousing, logistics optimization and social values and ethics have the tendency to
influence supply chain sustainability and economic performance and not vice versa (Guide
and Ketokivi, 2015; Dong et al., 2016; Dubey et al., 2017a, b). Hence, we postulate that there is
nonexistence of endogeneity in this context. Moreover, considering the non-normally
distributed nature of our exogenous constructs, we employed the Gaussian copula approach
to test endogeneity in the endogenous constructs. We assessed the copula coefficient
significance to determine existence of endogeneity using bootstrapping (Ebbes et al., 2016).
The copula coefficients obtained for the constructs is insignificant, which suggests that the
study is free from a possible endogeneity (Hausman, 1978; Park and Gupta, 2012).
4. Data analysis, results and discussions
4.1 Data analysis
The study used the partial least square structural equation modeling (PLS-SEM) technique
(SmartPLS software 3.2.8) to analyze the data. Peng and Lai (2012) claim that the application
of PLS-SEM in management research has seen an increment in recent times. According to
Hair et al. (2012) and Hair et al. (2017), PLS-SEM is appropriate for an explorative study.
Consequently, Hair et al. (2012) suggest that PLS-SEM is mainly used for predicting main
target variables or ascertaining primary drivers of constructs. However, Hair et al. (2017)
argue that covariance based structural equation modeling (CB-SEM) is more suitable for
“theory testing and confirmation.” Since this study is explorative in nature and seeks to
predict the influence of green warehousing, logistics optimization and social values and ethics
on supply chain sustainability and economic performance, PLS-SEM is suitable (Hair et al.,
2012; Green et al., 2019a, b; Hair et al., 2017; Baah, 2019). The analysis involves the evaluation
of the measurement model and structural model, where the measurement model assessment
involves finding the validity and reliability of the constructs in the model. The reliability and
validity of the model are examined through convergent validity, internal consistency
reliability and discriminant validity reliability. The structural model evaluation consists of
the assessment of the predictive relevance (Q2), variance explained (R2) and effect size (F2) of
green warehousing, social values and ethics and logistics optimization on supply chain
sustainability and economic performance. The global model fit was tested using the goodness
of fit (GoF). The analysis was done through calculating a Pls algorithm with 300 iterations,
blindfolding with a D value of 6 and bootstrapping with a subsample size of 5,000 (see
Table 3).
4.1.1 Measurement model assessment. The model was assessed to determine the validity
and reliability of the constructs using convergent validity, discriminant validity, internal
consistency reliability and indicator reliability. The convergent validity of the model was
Construct
Green
warehousing
Green
warehousing
Logistics
optimization
Economic
performance
Social values and
ethics
Supply chain
sustainability
Logistics
optimization
Economic
performance
Social
values and
ethics
1.879
1.525
1.731
1.564
1.975
1.916
1.661
1.847
1.871
1.957
1.768
1.937
1.865
1.607
1.652
2.631
2.613
2.656
Supply chain
sustainability
The role of
supply chain
sustainability
1.548
2.628
Table 3.
Inner VIF values
examined using the average variance extracted (AVE) whiles the indicator reliability was
determined using the factor loadings (Henseler et al., 2015). Internal consistency was assessed
using composite reliability and Cronbach’s alpha (Henseler, 2017). The thresholds used for
measuring the factor loadings, Cronbach’s alpha, composite reliability and AVE were >0.70,
>0.70, >0.70 and >0.50, respectively (Henseler et al., 2015). The least values for composite
reliability (0.860), Cronbach’s alpha (0.802), factor loadings (0.641) and AVE (0.550) indicate
that the scales used to measure the model in this study are reliable. The threshold of <0.85
was used to confirm the discriminant validity of each of the constructs (Henseler, 2017). The
results of the analysis suggested that the maximum HTMT ratio (0.733) show that the model
had achieved good discriminant validity. The values for the measurement variables are
shown in Table 4 and 5.
Construct
Green warehousing
Logistics optimization
Economic performance
Social values and ethics
Supply chain
sustainability
Construct
Logistics optimization
Economic
performance
Social values and
ethics
Supply chain
sustainability
Cronbach’s
alpha
Rho A
Composite
reliability
Average variance extracted
(AVE)
0.802
0.827
0.877
0.802
0.868
0.809
0.843
0.887
0.933
0.930
0.870
0.878
0.907
0.860
0.908
0.626
0.557
0.620
0.672
0.713
Green
warehousing
Logistics
optimization
Economic
performance
0.436
0.723
0.608
0.340
0.668
0.470
0.685
0.733
0.682
Table 4.
Construct reliability
and validity
Social values and
ethics
0.644
Table 5.
Heterotrait-monotrait
ratio (HTMT)
IJLM
4.1.2 Assessment of structural model. The structural model assessment comprised the
evaluation of the effect size, variance explained and the predictive relevance of the exogenous
variables (logistics optimization, green warehousing and social values and ethics) on the
endogenous variables (supply chain sustainability and economic performance) and the model
fit. The variance explained values (R2) were evaluated using the threshold of 0.25, 0.50 and
0.75, which means small, moderate and substantial, respectively (Hair et al., 2013). The R2
figures for supply chain sustainability (32.10%) and economic performance (52.10%) showed
that the model explained moderately of supply chain sustainability and moderate economic
performance. Besides, the f2 values of 0.212, 0.027 and 0.082 and 0.218, 0.049 and 0.051 show
the effect sizes of green warehousing, logistics optimization and social values and ethics on
the supply chain sustainability and economic performance, respectively. Finally, the
predictive relevance suggested by Stone-Geisser’s Q2 value (Stone, 1974; Geisser, 1974) was
evaluated using an omission distance (D) value of 6, which falls within the range 5–12
suggested by the study (Hair et al., 2017). The Q2 values of 0.300 and 0.332 for both economic
performance and supply chain sustainability showed that the model has excellent predictive
relevance. Further, the analysis suggests a global model fit value of 41.37%. The
multicollinearity test was conducted using the variance-inflated factor. The highest inner
VIF value is 2.656, which is far below the threshold of 3.3 suggested by Kock (2015). This
suggests that multicollinearity is not a problem in this study.
5. Results
The results of the analysis indicate that the hypotheses H1, H2, H3, H4, H5, H7, H8, H9 and
H10 are statistically significant at 5%. However, hypothesis H6 is not statistically supported.
Moreover, H4 and H5 are rejected because the direct relationships between green
warehousing, logistics optimization and economic performance are negative. These results
are contradictory to our hypotheses. The analysis reveals that the hypothesis, which states
that green warehousing significantly and positively influences supply chain sustainability
(H1, β 5 0.371, t 5 7.836, p 5 0.000) is statistically supported. Besides, it is found that logistics
optimization positively and significantly influences supply chain sustainability (H2,
β 5 0.344, t 5 6.743, p 5 0.000). Hence, hypothesis H2 is supported. Similarly, the
hypothesis stating that social values and ethics has a positive and significant influence on
supply chain sustainability is statistically supported (H3, β 5 0.358, t 5 9.197, p 5 0.000).
Again, the result reveals that social values and ethics has insignificant positive influence on
economic performance (H6, β 5 0.173, t 5 1.853, p 5 0.2710), which does not support
hypothesis 6. In addition, the hypothesis which states that supply chain sustainability has a
significant and positive influence on economic performance is statistically supported (H7,
β 5 0.254, t 5 9.197, p 5 0.000). Moreover, the results suggest that logistics optimization
significantly and negatively influences economic performance (H4, β 5 0.213, t 5 2.730,
p 5 0.000), which does not support H4. Similarly, the hypothesis, which states that green
warehousing has a significant and positive influence on economic performance (H5,
β 5 0.408, t 5 5.690, p 5 0.000), is not supported since the result suggests a negative
relationship between the constructs. The study further evaluates the indirect influence of
green warehousing, social values and ethics and logistics optimization on economic
performance through supply chain sustainability. The outcome of the analysis shows that
supply chain sustainability plays partial competitive mediating roles between green
warehousing and economic performance (H8; t 5 2.691, p 5 0.007) and logistics optimization
and economic performance (H9; t 5 2.596, p 5 0.010). Hence, the results support the
hypotheses H8 and H9. Besides, the findings indicate that supply chain sustainability plays a
partial complementary mediating role between social values and ethics and economic
performance (H10, t 5 2.550, p 5 0.011). Hence, hypothesis H10 is statistically supported (see
Table 6 and 7).
Direct path
Hypothesis
Beta
(β)
T statistics (jO/
STDEVj)
P-values
Green warehousing - > Supply chain
sustainability
Logistics optimization - > Supply
chain sustainability
Social values and ethics - > Supply
chain sustainability
Logistics optimization - > Economic
performance
Green warehousing - > Economic
performance
Social values and ethics- > Economic
performance
Supply chain sustainability
- > Economic performance
H1
0.371
7.836
0.000
Supported
H2
0.344
6.743
0.000
Supported
H3
0.358
6.197
0.000
Supported
H4
0.213
4.630
0.000
H5
0.408
8.690
0.000
H6
0.173
1.853
0.271
H7
0.245
4.730
0.006
Not
supported
Not
supported
Not
supported
Supported
F2
0.001
T statistics
1.533
P-values
0.387
0.000
1.101
0.913
0.001
1.430
0.322
0.000
1.665
0.221
Results
The role of
supply chain
sustainability
Control variables
Nature of industry- > Supply chain
sustainability
Firm size - > Supply chain
sustainability
Nature of industry- > Economic
performance
Firm size - > Economic performance
Table 6.
Direct effect
T statistics (jO/
STDEVj)
P-values
H8
2.691
0.007
Supported
H9
2.569
0.010
Supported
H10
2.550
0.011
Supported
Indirect path
Hypothesis
Green warehousing - > Supply chain
sustainability - > Economic performance
Logistics optimization - > Supply chain
sustainability - > Economic performance
Social values and ethics - > Supply chain
sustainability - > Economic performance
Results
6. Discussions
The study uses the RDT as the theoretical lens of this study to explore the direct influence of
green warehousing, logistics optimization and social values and ethics on supply chain
sustainability as well as the direct and indirect influence of green warehousing, logistic
optimization and social values and ethics on economic performance. The findings reveal that
green warehousing, logistics optimization and social values and ethics significantly improve
supply chain sustainability (H1, H2, H3). This suggests that the implementation of
sustainable supply chain practices such as logistics optimization, green warehousing and
social values and ethics require the collaborative efforts of supply chain partners achieve
supply chain sustainability results. This advances the position of the RDT that relationship
between supply chain partners is essential for sustainability development (Ulrich and
Barney, 1984). Hence, the manufacturing firms in Ghana collaborate with suppliers and
customers to implement green warehousing, social values and ethics and logistics
optimization to reduce waste, minimize greenhouse gas emissions, ensure energy and
Table 7.
Mediation
IJLM
resource conservation and improve societal and employee safety and wellbeing. Ries et al.
(2018) suggests that logistics activities contribute to about 5.5% to 13% of the entire global
supply chain emissions. The implementation of green warehousing may serve as a solution to
logistics emission through reduced gas emission, air pollution, which improves supply chain
sustainability. This finding is consistent with extant literature (Agyabeng-Mensah et al.,
2020a, b, c, d; Zaid et al., 2018), which establish that green practices substantially improve
supply chain sustainability. Again, the findings indicate that the effective implementation of
logistics optimization as a green practice requires collaboration along the supply chain to
achieve desired sustainability results. Hence, logistics optimization implemented with the
collaborative efforts of supply chain members leads to significant reduction in greenhouse
gas emission and adverse impact of firms’ operation on the environment and the health and
wellbeing of members of the society. This indicates that collaboration with customers and
suppliers in the implementation of logistics optimization improve performance as posited by
RDT. Also, social values and ethics are usually adopted to ensure the welfare and safety of
employees and society (Croom et al., 2018). For a firm to develop and implement effective
social value and ethics along the supply chain to achieve excellent results, the role of
customers and suppliers are required. This reveals that an increase in sharing of social values
and ethics among supply chain partners facilitate the achievement of expected supply chain
sustainability goals, which represents the position of RDT. Besides, our result reveals that
social values and ethics contribute to the advancement of economic performance (H6).
However, the influence of social values and ethics on economic performance is insignificant.
This may be due to the fact that social values and ethics takes longtime to reflect in the
economic performance. On the contrary, the results reveal that green warehousing and
logistics optimization have significant negative influence on economic performance (H4-H5).
This indicates that green practices that are implemented solely to gain economic benefits may
adversely affect the economic performance. The above findings answer the first question of
our study, which forms a key contribution of this study.
Consequently, the second question of this study is answered in this section. The study’s
findings reveal that supply chain sustainability significantly improves economic
performance while it mediates the influence of green warehousing, logistics optimization
and social values and ethics on economic performance (H7, H8, H9, H10). These findings are
consistent with extant literature (Afum et al., 2020; Feng et al., 2018; Agyabeng-Mensah et al.,
2020a, b, c, d; Yu et al., 2020). This confirms that the implementation of green warehousing
and logistics optimization ensure societal and employee safety as well as protect the
environment from destruction and ensure growth in market size, sales, profitability and
return on investment. This may lead to improvement in societal acceptance (Kumar and
Shekhar, 2015) and result in the retention of customers and the attraction of new customers
relative to competitors leading to improved economic performance. Social values and ethics
improves the welfare of employees and society members, which protects the image of a firm
and attracts loyal customers and employees. This may lead to savings in the cost of
recruitment due to reduced labor turnover rate and improve sales and profit margins
(Agyabeng-Mensah et al., 2020a, b, c, d). The findings of the study has establish that the
concerted effort from suppliers and customers of a firm toward the implementation of green
warehousing, logistics optimization and social values and ethics is required to achieve
substantial improvement in supply chain sustainability goals as posited by the RDT. Besides,
the findings indicate that despite the level of collaboration among supply chain partners toward
the implementation of green warehousing and logistics optimization the firm’s economic
performance may be adversely affected. Moreover, social values and ethics have insignificant
influence on economic performance. These results defeat the position of RDT in situations
where the adoption of green practices do not address significant environmental and social
problems which may translate into improved economic performance. Hence, we establish that
based on the RDT, the effort of supply chain partners are required for effective implementation
of green warehousing, logistics optimization and social values and ethics that advances supply
chain sustainability in order to achieve significant economic performance goals.
7. Conclusion
7.1 Theoretical implications
The study advances the literature in green supply chain management by developing and
testing a model that explores the relationships between green warehousing, logistics
optimization social values and ethics, supply chain sustainability and economic performance
from the developing country’s perspective. Hence, this study serves as a response to
researches requesting studies in sustainability from emerging economies, especially, Africa
(Tseng et al., 2019; Bartolini et al., 2019; Dubey et al., 2017a, b). To the best of our knowledge,
this is the first study that empirically tests the influence of green warehousing, social values
and ethics and logistics optimization on both supply chain sustainability and economic
performance through the RDT lens. Particularly, this study has revealed that RDT is an
important theory that brings understanding to the collaboration between a focal firm and its
supply chain partners. Essentially, RDT suggests that firms should work with their supply
chain members in a way that facilitates resource sharing among them to achieve improved
firm performance. Based on our findings, we posit that green warehousing, logistics
optimization and social values and ethics are sustainable supply chain management practices
that advance firm performance (supply chain sustainability and economic performance). In
applying RDT to green warehousing, logistics optimization and social values and ethics
context, we reveal that RDT can be effectively employed in a new context, which shows a new
area of application. This addresses the recommendation by Pfeffer (2005) to invoke RDT to
ensure its continuous relevance in literature. Moreover, this study advances the scope of the
applicability of RDT (Singh et al., 2011). It has been shown in this study that RDT is
applicable in explaining management tool at the operational and strategic levels which is
different from extant empirical studies focusing on only strategic level actions such as strategic
alliances (Gulati and Gargiulo, 1999) and mergers and acquisitions (Finkelstein, 1997).
7.2 Managerial implications
The study may also contributes to the work of managers. The findings offer a logical basis for
the adoption of green warehousing, logistics optimization and social values and ethics as
supply chain sustainability strategies. Besides, this study serves as a blueprint and
persuasive justification for managers to push for the adoption of social and ethical policies
and practices in supply chain. Moreover, the study encourages managers to adopt logistics
optimization, green warehousing and social values and ethics to improve supply chain
sustainability. Besides, the study encourages firms to collaborate with their suppliers and
customers to share resources in the implementation of green warehousing, logistics
optimization and social values and ethics to achieve significant waste and gas emission
reduction, improved welfare of employee and community members and increased sales,
market share, profitability and return on investment. In addition, firms can effectively
manage their relationships with supply chain members to convert them into sources of
competitive advantage to advance economic performance. Further, while working with
supply chain members (customers and suppliers) during the implementation of green
warehousing, logistics optimization, and social values and ethics, firm’s employees are likely
to develop skills and green capabilities to create competitive advantage and improve
economic performance. This suggests that firms should not undermine the role of
collaboration with suppliers and customers in the effective implementation of green
warehousing, logistics optimization and social values and ethics. The findings indicate that
The role of
supply chain
sustainability
IJLM
green warehousing and logistics optimization have adverse influence on economic
performance. However, supply chain sustainability provides competitive mediation role
between both logistics optimization and green warehousing and economic performance. This
suggests that firms should adopt sustainable practices that have both social and
environmental bearing to be able to advance economic performance.
7.3 Implication for society
The researchers hold the belief that this study makes significant contributions to the entire
society. Logistics activities of firms significantly contribute to greenhouse emissions and
black air that are harmful to the health of people. This study suggests that green warehousing
and logistics optimization reduce environmental pollution, waste and emission of harmful
gases. This leads to improved health of the members of the society. Besides, the use of green
energy and the reduction of waste and energy consumption help firms to meet the needs of
final customers and other stakeholders in a way that conserve resources for the unborn
generation and advance the ecology of our planet. Moreover, social values and ethics have a
positive influence on supply chain sustainability, which suggests that this study unveils
practices that seek the welfare and safety of employees and community members.
7.4 Limitation of the study and future direction
This study has several limitations, which need to be addressed in future studies. The findings
of the study suffer from limited generalizability. This is because the study was conducted in
only manufacturing firms in Ghana. Future research could extend the study to other
industries like the logistics industry. Again, though the sample size was adequate for the PLSSEM and adequately represented the manufacturing industry in Ghana, the response rate
was relatively low. Future study may try to increase the response rate to add more credence to
the findings. Though the results for common method bias and multicollinearity analysis
showed that the model had good collinearity and suffered no common method bias, however,
there may be the existence of a problem of multicollinearity due to the similarities in some of
the measuring variables. The framework could also be tested in other industries and
geographical areas. Researchers could test the framework by finding the impact of the
antecedent variables on other measures of economic performance in future research.
References
Abushaikha, I. (2018), “The influence of logistics clustering on distribution capabilities: a qualitative
study”, International Journal of Retail and Distribution Management, Vol. 46 No. 6, pp. 577-594,
doi: 10.1108/IJRDM-01-2018-0018.
Adomako, S., Amankwah-Amoah, J., Danso, A., Konadu, R. and Owusu-Agyei, S. (2019),
“Environmental sustainability orientation and performance of family and nonfamily firms”,
Business Strategy and the Environment, Vol. 28 No. 6, pp. 1250-1259.
Afum, E., Agyabeng-Mensah, Y., Sun, Z., Frimpong, B., Kusi, L.Y. and Acquah, I.S.K. (2020),
“Exploring the link between green manufacturing, operational competitiveness, firm reputation
and sustainable performance dimensions: a mediated approach”, Journal of Manufacturing
Technology Management, doi: 10.1108/JMTM-02-2020-0036.
Agyabeng-Mensah, Y., Ahenkorah, E.N.K. and Agnikpe, M.C.G. (2019a), “The intermediary role of
supply chain capability between supply chain integration and firm performance”, Journal of
Supply Chain Management Systems, Vol. 8 No. 2.
Agyabeng-Mensah, Y., Ahenkorah, E.N.K. and Korsah, G.N.A. (2019b), “The mediating roles of supply
chain quality integration and green logistics management between information technology and
organisational performance”, Journal of Supply Chain Management Systems, Vol. 8 No. 4.
Agyabeng-Mensah, Y., Ahenkorah, E.N.K. and Osei, E. (2019c), “Impact of logistics information
technology on organisational performance: mediating role of supply chain integration and
customer satisfaction”, Journal of Supply Chain Management Systems, Vol. 8 No. 4.
Agyabeng-Mensah, Y., Afum, E., Agnikpe, C., Cai, J., Ahenkorah, E. and Dacosta, E. (2020a),
“Exploring the mediating influences of total quality management and just in time between
green supply chain practices and performance”, Journal of Manufacturing Technology
Management, doi: 10.1108/JMTM-03-2020-0086.
Agyabeng-Mensah, Y., Afum, E. and Ahenkorah, E. (2020b), “Exploring financial performance and
green logistics management practices: examining the mediating influences of market,
environmental and social performances”, Journal of Cleaner Production, p. 120613.
Agyabeng-Mensah, Y., Ahenkorah, E., Afum, E., Agnikpe, C. and Adu, N.A. (2020c), “Examining the
influence of internal green supply chain practices, green human resource management and
supply chain environmental cooperation on firm performance”, Supply Chain Management:
International Journal. doi: 10.1108/SCM-11-2019-0405.
Agyabeng-Mensah, Y., Ahenkorah, E. Afum, E. and Owusu, D. (2020d), “The influence of lean
management and environmental practices on relative competitive quality advantage and
performance”, Journal of Manufacturing Technology Management. doi: 10.1108/JMTM-122019-0443.
Allouche, J. and Laroche, P. (2005), “A meta-analytical investigation of the relationship between
corporate social and financial performance”.
Amemba, C.S., Nyaboke, P.G., Osoro, A. and Mburu, N. (2013), “Elements of green supply chain
management”, European Journal of Business and Management, Vol. 5 No. 12, pp. 51-61.
Armstrong, J.S. and Overton, T.S. (1977), “Estimating nonresponse bias in mail surveys”, Journal of
Marketing Research, Vol. 14 No. 3, pp. 396-402.
Baah, C. (2019), “Green logistics and organisational performance: exploring time-based competition as
a missing link”, Journal of Supply Chain Management Systems, Vol. 8 No. 3.
Baah, C. and Jin, Z. (2019), “Sustainable supply chain management and organizational performance:
the intermediary role of competitive advantage”, J. Mgmt. and Sustainability, Vol. 9, p. 119.
Baah, C., Jin, Z. and Tang, L. (2020), “Organizational and regulatory stakeholder pressures friends or
foes to green logistics practices and financial performance: investigating corporate reputation
as a missing link”, Journal of Cleaner Production, Vol. 247, 119125.
Bartolini, M., Bottani, E. and Eric, H. (2019), “Green warehousing: systematic literature review and
bibliometric analysis”, Journal of Cleaner Production.
Bastas, A. and Liyanage, K. (2019), “Integrated quality and supply chain management business
diagnostics for organizational sustainability improvement”, Sustainable Production and
Consumption, Vol. 17, pp. 11-30.
Beamon, B.M. (2005), “Environmental and sustainability ethics in supply chain management”, Science
and Engineering Ethics, Vol. 11 No. 2, pp. 221-234.
Boix, M., Montastruc, L., Azzaro-Pantel, C. and Domenech, S. (2015), “Optimisation methods applied to
the design of eco-industrial parks: a literature review”, Journal of Cleaner Production, Vol. 87,
pp. 303-317.
Cantor, D.E., Morrow, P.C. and Montabon, F. (2012), “Engagement in environmental behaviours
among supply chain management employees: an organisational support theoretical
perspective”, Journal of Supply Chain Management, Vol. 48 No. 3, pp. 33-51.
Cao, M., Vonderembse, M.A., Zhang, Q. and Ragu-Nathan, T.S. (2010), “Supply chain collaboration:
conceptualisation and instrument development”, International Journal of Production Research,
Vol. 48 No. 22, pp. 6613-6635.
Carrizo-Moreira, A. (2014), “Single minute exchange of die and organizational innovation in seven
small and medium-sized firms”, Lean Manufacturing in the Developing World, Springer, Cham,
pp. 483-499.
The role of
supply chain
sustainability
IJLM
Carter, C.R. and Rogers, D.S. (2008), “A framework of sustainable supply chain management: moving
toward new theory”, International Journal of Physical Distribution and Logistics Management,
Vol. 38 No. 5, pp. 360-387, doi: 10.1108/09600030810882816.
Chen, I.J. and Paulraj, A. (2004), “Towards a theory of supply chain management: the constructs and
measurements”, Journal of Operations Management, Vol. 22 No. 2, pp. 119-150.
Chiou, T.Y., Chan, H.K., Lettice, F. and Chung, S.H. (2011), “The influence of greening the
suppliers and green innovation on environmental performance and competitive advantage in
Taiwan”, Transportation Research Part E: Logistics and Transportation Review, Vol. 47 No. 6,
pp. 822-836.
Colicchia, C., Melacini, M. and Perotti, S. (2011), “Benchmarking supply chain sustainability: insights
from a field study”, Benchmarking: An International Journal, Vol. 18 No. 5, pp. 705-732.
Cox, S. and Graham, L. (2010), “Sustainability measured: gauging the energy efficiency of European
warehouses”, Pro Logis Research Insights.
Coyle, J.J., Gibson, B.J., Langley, C.J. and Novack, R.A. (2013), Managing Supply Chains: A Logistics
Approach, South-Western Cengage Learning.
Croom, S., Vidal, N., Spetic, W., Marshall, D. and McCarthy, L. (2018), “Impact of social sustainability
orientation and supply chain practices on operational performance”, International Journal of
Operations and Production Management, Vol. 38 No. 12, pp. 2344-2366, doi: 10.1108/IJOPM-032017-0180.
Dekker, S. (2012), Just Culture: Balancing Safety and Accountability, Ashgate Publishing.
Dong, F., Hennessy, D.A., Jensen, H.H. and Volpe, R.J. (2016), “Technical efficiency, herd size, and exit
intentions in US dairy farms”, Agricultural Economics, Vol. 47 No. 5, pp. 533-545.
Dowlatshahi, S. (2000), “Developing a theory of reverse logistics”, Interfaces, Vol. 30 No. 3, pp. 143-155.
Drake, M.J. and Schlachter, J.T. (2008), “A virtue-ethics analysis of supply chain collaboration”, Journal
of Business Ethics, Vol. 82 No. 4, pp. 851-864.
Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Hazen, B., Giannakis, M. and Roubaud, D.
(2017a), “Examining the effect of external pressures and organizational culture on shaping
performance measurement systems (PMS) for sustainability benchmarking: some empirical
findings”, International Journal of Production Economics, Vol. 193, pp. 63-76.
Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K.T. and Wamba, S.F. (2017b),
“Supply chain sustainability: framework and further research directions”, Journal of Cleaner
Production, Vol. 142, pp. 1119-1130.
Ebbes, P., Papies, D. and van Heerde, H.J. (2016), “Dealing with endogeneity: a nontechnical guide for
marketing researchers”, Handbook of Market Research.
Eriksson, H., Conand, C., Lovatelli, A., Muthiga, N.A. and Purcell, S.W. (2015), “Governance
structures and sustainability in Indian Ocean sea cucumber fisheries”, Marine Policy, Vol. 56,
pp. 16-22.
Feng, M., Yu, W., Wang, X., Wong, C.Y., Xu, M. and Xiao, Z. (2018), “Green supply chain management
and financial performance: the mediating roles of operational and environmental performance”,
Business Strategy and the Environment, Vol. 27 No. 7, pp. 811-824.
Finkelstein, S. (1997), “Interindustry merger patterns and resource dependence: a replication and
extension of Pfeffer (1972)”, Strategic Management Journal, Vol. 18 No. 10, pp. 787-810.
Garetti, M. and Taisch, M. (2012), “Sustainable manufacturing: trends and research challenges”,
Production Planning and Control, Vol. 23 Nos 2-3, pp. 83-104.
Garriga, E. and Mele, D. (2004), “Corporate social responsibility theories: mapping the territory”,
Journal of Business Ethics, Vol. 53 Nos 1-2, pp. 51-71.
Geisser, S. (1974), “A predictive approach to the random effect model”, Biometrika, Vol. 61 No. 1,
pp. 101-107.
Gonenc, H. and Scholtens, B. (2017), “Environmental and financial performance of fossil fuel firms: a
closer inspection of their interaction”, Ecological Economics, Vol. 132, pp. 307-328.
Gonzalez, P., Sarkis, J. and Adenso-Dıaz, B. (2008), “Environmental management system certification
and its influence on corporate practices”, International Journal of Operations and Production
Management.
Gonzalez-Torre, P.L., Adenso-Dıaz, B. and Artiba, H. (2004), “Environmental and reverse logistics
policies in European bottling and packaging firms”, International Journal of Production
Economics, Vol. 88 No. 1, pp. 95-104.
Gotschol, A., De Giovanni, P. and Vinzi, V.E. (2014), “Is environmental management an economically
sustainable business?”, Journal of Environmental Management, Vol. 144, pp. 73-82.
Green, K.W., Inman, R.A., Sower, V.E. and Zelbst, P.J. (2019a), “Impact of JIT, TQM and green supply chain
practices on environmental sustainability”, Journal of Manufacturing Technology Management.
Green, K.W., Inman, R.A., Sower, V.E. and Zelbst, P.J. (2019b), “Comprehensive supply chain
management model”, Supply Chain Management: International Journal.
Gu, Y., Wu, Y., Xu, M., Mu, X. and Zuo, T. (2016), “Waste electrical and electronic equipment (WEEE)
recycling for a sustainable resource supply in the electronics industry in China”, Journal of
Cleaner Production, Vol. 127, pp. 331-338.
Guide, V.D.R., Jr and Ketokivi, M. (2015), “Notes from the editors: restructuring the journal of
operations management”, Journal of Operations Management, Vol. 38, pp. v-x.
Gulati, R. and Gargiulo, M. (1999), “Where do interorganizational networks come from?”, American
Journal of Sociology, Vol. 104 No. 5, pp. 1439-1493.
Gunasekaran, A. and Spalanzani, A. (2012), “Sustainability of manufacturing and services:
investigations for research and applications”, International Journal of Production Economics,
Vol. 140 No. 1, pp. 35-47.
Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2012), “An assessment of the use of partial least
squares structural equation modeling in marketing research”, Journal of the Academy of
Marketing Science, Vol. 40 No. 3, pp. 414-433.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2013), “Partial least squares structural equation modelling:
rigorous applications, better results and higher acceptance”, Long Range Planning, Vol. 46
Nos 1-2, pp. 1-12.
Hair, J.F., Jr, Matthews, L.M., Matthews, R.L. and Sarstedt, M. (2017), “PLS-SEM or CB-SEM: updated
guidelines on which method to use”, International Journal of Multivariate Data Analysis, Vol. 1
No. 2, pp. 107-123.
and Kovacs, G. (2010), “The sustainable agenda and energy efficiency: logistics
Halldorsson, A.
solutions and supply chains in times of climate change”, International Journal of Physical
Distribution and Logistics Management, Vol. 40 Nos 1/2, pp. 5-13.
Hansen, Z.N.L., Larsen, S.B., Nielsen, A.P., Groth, A., Gregersen, N.G. and Ghosh, A. (2018), “Combining
or separating forward and reverse logistics”, International Journal of Logistics Management.
Harris, I., Mumford, C.L. and Naim, M.M. (2014), “A hybrid multi-objective approach to capacitated
facility location with flexible store allocation for green logistics modeling”, Transportation
Research Part E: Logistics and Transportation Review, Vol. 66, pp. 1-22.
Hausman, J.A. (1978), “Specification tests in econometrics”, Econometrica: Journal of the Econometric
Society, pp. 1251-1271.
Henseler, J. (2017), “Bridging design and behavioral research with variance-based structural equation
modeling”, Journal of Advertising, Vol. 46 No. 1, pp. 178-192.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity
in variance-based structural equation modelling”, Journal of the Academy of Marketing Science,
Vol. 43 No. 1, pp. 115-135.
The role of
supply chain
sustainability
IJLM
Hishan, S.S., Khan, A., Ahmad, J., Hassan, Z.B., Zaman, K. and Qureshi, M.I. (2019), “Access to clean
technologies, energy, finance, and food: environmental sustainability agenda and its implications
on Sub-Saharan African countries”, Environmental Science and Pollution Research, Vol. 26 No. 16,
pp. 16503-16518.
Hoejmose, S., Brammer, S. and Millington, A. (2013), “An empirical examination of the relationship
between business strategy and socially responsible supply chain management”, International
Journal of Operations and Production Management, Vol. 33 No. 5, pp. 589-621, doi: 10.1108/
01443571311322733.
Indrawati, D., Lindu, M. and Denita, P. (2018), “Potential of solid waste utilization as source of refuse
derived fuel (RDF) energy (case study at temporary solid waste disposal site in West Jakarta)”,
IOP Conference Series: Earth and Environmental Science, Vol. 106, IOP Publishing, p. 012103,
January.
Inman, R.A. and Green, K.W. (2018), “Lean and green combine to impact environmental and
operational performance”, International Journal of Production Research, Vol. 56 No. 14,
pp. 4802-4818.
Khan, M.I., Chhetri, A.B. and Islam, M.R. (2007), “Community-based energy model: a novel approach to
developing sustainable energy”, Energy Sources, Part B, Vol. 2 No. 4, pp. 353-370.
Khan, S.A., Kusi-Sarpong, S., Arhin, F.K. and Kusi-Sarpong, H. (2018), “Supplier sustainability
performance evaluation and selection: a framework and methodology”, Journal of Cleaner
Production, Vol. 205, pp. 964-979.
Khan, I. (2019), “Power generation expansion plan and sustainability in a developing country: a multicriteria decision analysis”, Journal of Cleaner Production, Vol. 220, pp. 707-720.
Kock, N. (2015), “Common method bias in PLS-SEM: a full collinearity assessment approach”,
International Journal of E-Collaboration, Vol. 11 No. 4, pp. 1-10.
Kumar, R. and Shekhar, S. (2015), “Implementation of green supply chain management in steel
industries in Chhattisgarh”, International Journal of Advanced. Engineering Resarch Studies/IV/
II/Janunary-March, Vol. 259, p. 260.
Liang, S. and Yuyan, W. (2007), “To analyse on evolutionary game of green supply chain”, Value
Engineering, Vol. 5, pp. 65-69.
Liu, S., Kasturiratne, D. and Moizer, J. (2012), “A hub-and-spoke model for multi-dimensional
integration of green marketing and sustainable supply chain management”, Industrial
Marketing Management, Vol. 41 No. 4, pp. 581-588.
Longoni, A., Luzzini, D. and Guerci, M. (2018), “Deploying environmental management across
functions: the relationship between green human resource management and green supply chain
management”, Journal of Business Ethics, Vol. 151 No. 4, pp. 1081-1095.
Lu, Y. and Abeysekera, I. (2014), “Stakeholders’ power, corporate characteristics, and social and
environmental disclosure: evidence from China”, Journal of Cleaner Production, Vol. 64,
pp. 426-436.
Makov, T. and Newman, G.E. (2016), “Economic gains stimulate negative evaluations of corporate
sustainability initiatives”, Nature Climate Change, Vol. 6 No. 9, pp. 844-846.
McKinnon, A. (2010), Environmental Sustainability. Green Logistics: Improving the Environmental
Sustainability of Logistics, London.
Muller, A. (2009), “Sustainable agriculture and the production of biomass for energy use”, Climatic
Change, Vol. 94 Nos 3-4, pp. 319-331.
Nawanir, G., Teong, L.K. and Othman, S.N. (2013), “Impact of lean practices on operations
performance and business performance: some evidence from Indonesian manufacturing
companies”, Journal of Manufacturing Technology Management, Vol. 24 No. 7, pp. 1019-1050.
Neto, J.Q.F., Bloemhof-Ruwaard, J.M., van Nunen, J.A. and van Heck, E. (2008), “Designing and
evaluating sustainable logistics networks”, International Journal of Production Economics,
Vol. 111 No. 2, pp. 195-208.
Niknejad, A. and Petrovic, D. (2014), “Optimisation of integrated reverse logistics networks with
different product recovery routes”, European Journal of Operational Research, Vol. 238 No. 1,
pp. 143-154.
Nikolaou, I.E., Evangelinos, K.I. and Allan, S. (2013), “A reverse logistics social responsibility
evaluation framework based on the triple bottom line approach”, Journal of Cleaner Production,
Vol. 56, pp. 173-184.
Nilsson, H. (2013), Integrating Sustainability in the Food Supply Chain: Two Measures to Reduce the
Food Wastage in a Swedish Retail Store.
Ninlawan, C., Seksan, P., Tossapol, K. and Pilada, W. (2010), “The implementation of green supply chain
management practices in the electronics industry”, World Congress on Engineering 2012, July 4-6,
2012, London, UK, International Association of Engineers, Vol. 2182, March, pp. 1563-1568.
Park, S. and Gupta, S. (2012), Comparison of SML and GMM estimators for the random coefficient
logit model using aggregate data”, Empirical Economics, Vol. 43 No. 3, pp. 1353-1372.
Peng, D.X. and Lai, F. (2012), “Using partial least squares in operations management research: a
practical guideline and summary of past research”, Journal of Operations Management, Vol. 30
No. 6, pp. 467-480.
Pfeffer, J. (2005), “Developing resource dependence theory: how theory is affected by its environment”,
Great minds in management: The process of theory development, pp. 436-459.
Pfeffer, J. and Salancik, G.R. (2003), The External Control of Organizations: A Resource Dependence
Perspective, Stanford University Press.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in
behavioral research: a critical review of the literature and recommended remedies”, Journal of
Applied Psychology, Vol. 88 No. 5, p. 879.
Ries, J.M., Grosse, E.H. and Fichtinger, J. (2017), “Environmental impact of warehousing: a scenario
analysis for the United States”, International Journal of Production Research, Vol. 55 No. 21,
pp. 6485-6499.
Salancik, G.R. and Pfeffer, J. (1978), “A social information processing approach to job attitudes and
task design”, Administrative Science Quarterly, pp. 224-253.
Sarkis, J., Zhu, Q. and Lai, K.H. (2011), “An organisational theoretic review of green supply chain
management literature”, International Journal of Production Economics, Vol. 130 No. 1, pp. 1-15.
Shang, K.C., Lu, C.S. and Li, S. (2010), “A taxonomy of green supply chain management capability
among electronics-related manufacturing firms in Taiwan”, Journal of Environmental
Management, Vol. 91 No. 5, pp. 1218-1226.
Simper, N., Frank, B., Kaupp, J., Mulligan, N. and Scott, J. (2019), “Comparison of standardized
assessment methods: logistics, costs, incentives and use of data”, Assessment and Evaluation in
Higher Education, Vol. 44 No. 6, pp. 821-834.
Singh, P.J., Power, D. and Chuong, S.C. (2011), “A resource dependence theory perspective of ISO 9000
in managing organizational environment”, Journal of Operations Management, Vol. 29 Nos 1-2,
pp. 49-64.
Soytas, M.A., Denizel, M. and Usar, D.D. (2019), “Addressing endogeneity in the causal relationship
between sustainability and financial performance”, International Journal of Production
Economics, Vol. 210, pp. 56-71.
Stone, M. (1974), “Cross‐validatory choice and assessment of statistical predictions”, Journal of the
Royal Statistical Society: Series B (Methodological), Vol. 36 No. 2, pp. 111-133.
Tan, K.S., Ahmed, M.D. and Sundaram, D. (2009), “Sustainable warehouse management”, Proceedings
of the International Workshop on Enterprises and Organizational Modeling and Simulation,
ACM, p. 8, June.
Tang, J., Ji, S. and Jiang, L. (2016), “The design of a sustainable location-routing-inventory model
considering consumer environmental behavior”, Sustainability, Vol. 8 No. 3, p. 211.
The role of
supply chain
sustainability
IJLM
Torabizadeh, M., Yusof, N.M., Ma’aram, A. and Shaharoun, A.M. (2020), “Identifying sustainable
warehouse management system indicators and proposing new weighting method”, Journal of
Cleaner Production, Vol. 248, 119190.
Tseng, M.L., Wu, K.J., Lim, M.K. and Wong, W.P. (2019), “Data-driven sustainable supply chain
management performance: a hierarchical structure assessment under uncertainties”, Journal of
Cleaner Production, Vol. 227, pp. 760-771.
Ulrich, D. and Barney, J.B. (1984), “Perspectives in organizations: resource dependence, efficiency, and
population”, Academy of Management Review, Vol. 9 No. 3, pp. 471-481.
Vijayan, G. (2014), “Sustainability practices in the Malaysian grocery retail industry”, IAC2014,
Extended Abstract Template, pp. 1-4, Agricongress.
Villalonga, B. and McGahan, A.M. (2005), “The choice among acquisitions, alliances, and divestitures”,
Strategic Management Journal, Vol. 26 No. 13, pp. 1183-1208.
Wang, Z., Subramanian, N., Gunasekaran, A., Abdulrahman, M.D. and Liu, C. (2015), “Composite
sustainable manufacturing practice and performance framework: Chinese auto-parts suppliers’
perspective”, International Journal of Production Economics, Vol. 170, pp. 219-233.
Wong, C.W., Lai, K.H., Shang, K.C., Lu, C.S. and Leung, T.K.P. (2012), “Green operations and the
moderating role of environmental management capability of suppliers on manufacturing firm
performance”, International Journal of Production Economics, Vol. 140 No. 1, pp. 283-294.
Wu, H.J. and Dunn, S.C. (1995), “Environmentally responsible logistics systems”, International Journal
of Physical Distribution and Logistics Management.
Yildiz Çankaya, S.Y. and Sezen, B. (2019), “Effects of green supply chain management practices on
sustainability performance”, Journal of Manufacturing Technology Management, Vol. 30 No. 1,
pp. 98-121, doi: 10.1108/JMTM-03-2018-0099.
Yu, W., Chavez, R., Feng, M., Wong, C.Y. and Fynes, B. (2020), “Green human resource management
and environmental cooperation: an ability-motivation-opportunity and contingency
perspective”, International Journal of Production Economics, Vol. 219, pp. 224-235.
Zaid, A.A., Jaaron, A.A. and Bon, A.T. (2018), “The impact of green human resource management and
green supply chain management practices on sustainable performance: an empirical study”,
Journal of Cleaner Production, Vol. 204, pp. 965-979.
Zailani, S., Jeyaraman, K., Vengadasan, G. and Premkumar, R. (2012), “Sustainable supply chain
management (SSCM) in Malaysia: a survey”, International Journal of Production Economics,
Vol. 140 No. 1, pp. 330-340.
Zhang, K.Q. and Chen, H.H. (2017), “Environmental performance and financing decisions impact on
sustainable financial development of Chinese environmental protection enterprises”,
Sustainability, Vol. 9 No. 12, p. 2260.
Zhu, W., Chew, I.K. and Spangler, W.D. (2005), “CEO transformational leadership and organizational
outcomes: the mediating role of human–capital-enhancing human resource management”, The
Leadership Quarterly, Vol. 16 No. 1, pp. 39-52.
Zhu, Q., Sarkis, J. and Lai, K.H. (2008), “Green supply chain management implications for “closing the
loop”, Transportation Research Part E: Logistics and Transportation Review, Vol. 44
No. 1, pp. 1-18.
Zhu, Q., Feng, Y. and Choi, S.B. (2017), “The role of customer relational governance in environmental
and economic performance improvement through green supply chain management”, Journal of
Cleaner Production, Vol. 155, pp. 46-53.
Corresponding author
Yaw Agyabeng-Mensah can be contacted at: yawagyabeng830@gmail.com
Appendix
The role of
supply chain
sustainability
Variables
Measurement items
Items
Factor
loadings
Economic
performance
Reduced energy consumption
cost
Reduced inventory
management cost
Increased market share
Increased sales growth
Increased gross profit margin
Increased net profit return
Increased return on assets
Our firm uses biodegradable
packages in the warehouse
We use plant-based products
for packaging in our
warehouse
We have replaced plastics with
paper packages in our
warehouse
We Use LED bulbs in the
warehouse
We use solar energy in the
warehouse
We use waste management
systems in our warehouse
We practice just-in-time
concept in our warehouse
We use inventory
management system in our
warehouse
We reclaim products from
customers for
remanufacturing
We collect used products for
recycling
We recall used products for
proper disposal
We work with distributors to
build a optimized
transportation network
Our firm uses route
optimization software to
ensure efficiency
We cooperate with suppliers
and customers to develop
route networks
EP1
0.850
EP2
0.793
EP3
EP4
EP5
EP6
EP7
GW1
0.713
0.850
0.793
0.713
0.850
0.748
GW2
0.833
GW3
0.748
GW4
0.833
GW5
0.748
GW6
0.833
GW7
0.688
GW8
0.775
LO1
0.788
LO2
0.879
LO3
0.840
LO4
0.775
LO5
0.840
LO6
0.840
Green
warehousing
Logistics
optimization
Source of items
Agyabeng-Mensah et al. (2020a,
b, c, d), Nawanir et al. (2013),
Abushaikha (2018)
Coyle et al. (2013), Amemba et al.
(2013)
Nikolaou et al. (2013), Vijayan
(2014), Boix et al. (2015)
(continued )
Table A1.
Questionnaire
IJLM
Variables
Measurement items
Items
Factor
loadings
Supply chain
sustainability
Improvement in the firm’s
environmental situation
Reduction in energy usage
Reduction in wastewater
Improvement in the safety of
workers and society
Improvement the welfare of
employees and society
Improved equal job
opportunities in our firm
Reduction in carbon dioxide
emission
Our firm has developed
internal ethical purchasing
policies
We purchase raw materials
from only firms that do not
engage in child labor
There is job security policy for
employees
There is equal job opportunity
policies for employees
There is an internal ethical
sourcing policies in our firm
We cooperation with suppliers
for environmental objectives
We vet and audit suppliers
based on their ethical policies
Our firm organizes green
training programs for
employees
There are environmental
safety policies in our firm
SCS1
0.921
SCS2
SCS3
SCS4
0.850
0.850
0.788
SCS5
0.791
SCS6
0.780
SCS7
0.850
SVE1
0.810
SVE2
0.839
SVE3
0.773
SVE4
0.764
SVE5
0.810
SVE6
0.852
SVE7
0.793
SVE8
0.885
SVE9
0.799
Social values
and ethics
Table A1.
Source of items
Green et al. (2019a, b), Zaid et al.
(2018), Longoni et al. (2018)
Sarkis et al. (2011), Hoejmose et al.
(2013), Gunasekaran and
Spalanzani (2012), Eriksson et al.
(2015)