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sustainability
Article
A Strategic Knowledge Management Approach to
Circular Agribusiness
Dong-Her Shih 1ID , Chia-Ming Lu 1, *, Chia-Hao Lee 2, Yuh-Jiuan M. Parng 3, Kuo-Jui Wu 4, *ID
and Ming-Lang Tseng 5,*ID
1
Department of Information Management, National Yunlin University of Technology, Douliu 64002, Taiwan;
shihdh@yuntech.edu.tw
2Department of Finance, MingDao University, Changhua County 52345, Taiwan; lijiahao@mdu.edu.tw
3Department of Accounting and Information Systems, Asia University, Taichung City 41354, Taiwan;
melodyp@asia.edu.tw
4School of Business, Dalian University of Technology, Panjing 124221, China
5Institute of Innovation and Circular Economy, Asia University, Taichung City 41354, Taiwan
*Correspondence: joe@jiahsing.com.tw (C.-M.L.); wukuojui@dlut.edu.cn (K.-J.W.);
tsengminglang@gmail.com (M.-L.T.); Tel.: +886-91-0459836 (C.-M.L.); +86-138-0427-6853 (K.-J.W.);
+886-91-0309400 (M.-L.T.)
Received: 4 May 2018; Accepted: 30 June 2018; Published: 9 July 2018
Abstract:
In this study, we construct a theoretical model for strategic knowledge management in the
circular agribusiness industry. Previous studies lack an analysis of strategic knowledge management
and sets of measures. Hence, this study applies the fuzzy Technique for Order of Preference by
Similarity to Ideal Solution (known as fuzzy TOPSIS), and utilizes the interrelationship weights as
a parameter to justify the weights and address important attributes. The results demonstrate that
strategic knowledge management improves a firm’s competitive advantages through high-level
management support. The theoretical and managerial implications are that high-level management
support, firm performance, and knowledge management process cycles are the most important
strategic knowledge management aspects for improving a firm’s performance in circular agribusiness.
Keywords:
knowledge management; strategic knowledge management; circular agribusiness;
fuzzy TOPSIS
1. Introduction
Agribusiness plays an indispensable role in the world’s economy and has become the most
important source of food supplies [
1
]. However, the issue of sustainability has come to the forefront
in recent decades. In particular, Vietnam’s traditional agribusiness needs to enhance its competitive
advantage and work toward sustainability. The essential factor in enhancing competitive advantage
in agribusiness is the professionalization of management in production [
2
]. To achieve this purpose
and retain competitive advantages, strategic management is considered a potent means to analyze the
environment in order to identify and develop specialized strategies [
3
,
4
]. Al-Hakim and Shahizan [
5
]
emphasized that knowledge is one of the crucial resources for strategic management by helping
administrators understand a firm’s core characteristics and uphold its competitive position. In addition,
a firm creates current knowledge and then employs it effectively to create competitive advantages [
6
].
However, Del Junco, et al. [
7
] found that one of the main difficulties for firms regarding sustainable
development is failure in knowledge management (KM) due to poor strategic integration in practice.
Hence, as the ability of firms to identify, codify, leverage, and use their knowledge sources as a firm
strategy to increase overall performance and competitive advantages, strategic KM (SKM) has become
an important determinant to help firms improve their competitive advantages [8].
Sustainability 2018,10, 2389; doi:10.3390/su10072389 www.mdpi.com/journal/sustainability
Sustainability 2018,10, 2389 2 of 20
Prior studies have examined strategic management or general KM, but there have been few
empirical investigations of SKM [
6
,
9
]. Despite the extensive literature, Venkitachalam and Willmott [
10
]
argued that a conceptual understanding of the vital role of SKM in firms appears to be lacking,
and there is a considerable gap that must be closed in terms of both theory and practice. Conversely,
Garavelli, et al. [11]
indicated that SKM is often adopted and implemented, and then proposed a model
to assess the firm’s current status and support the identification of suitable actions to better implement
its SKM and improve the firm’s practices. Notably, Lwoga, et al. [
12
] argued that while SKM is based
on procuring, organizing and retaining explicit knowledge, the primary approach for continuation still
depends on the transfer of technology, creating an obstacle to academics and practitioners seeking to
combine knowledge and information systems. In addition, Tseng, et al. [
13
] presented a closed-loop
framework to improve a firm’s performance in terms of service supply chain management to achieve
greater sustainability. Green practices require the application of the closed-loop framework, particularly
for supply chain networks. Li et al. [
14
] noted the barriers and strategies in ecological industrial parks
utilizing closed-loop supply chain networks (known as a circular system). These industrial practices
require strong performance in the use of SKM, as prior studies imply that closed-loop sustainable
supply chain management (SSCM) needs to utilize SKM in order to improve performance.
However, to address SKM in closed-loop supply chain management (which is called circular),
the assessment attributes need to be collected into a framework. Such attributes are always described
in terms of qualitative information. Fuzzy set theory can be used to translate qualitative information
to a quantifiable scale. Nevertheless, many analytical methods have been utilized to evaluate
the attributes of SKM and develop SKM assessments. López-Nicolás and Meroño-Cerdán [
6
]
provided a conceptual model and structured questionnaire and conducted confirmatory factor analysis.
This study applies the advantages of fuzzy numbers to describe the values of attributes under
circumstances in which information is vague, imprecise, or uncertain, and it is very difficult to
precisely meet the attributes proposed by López-Nicolás and Meroño-Cerdán using real numbers and
values [
15
]. In particular, the fuzzy Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS) weights each attribute, normalizes the scores for each criterion, and measures the geometric
distance between each solution and its ideal solution. There are even a few studies that have involved
using interrelationship weight to justify the final importance, and the performance weights due to
attributes are similar to those of interrelationships [
14
]. Thus, this study fills gaps from previous
studies while maintaining the advantages of reducing decision-making weakness and increasing the
accuracy of the results.
Hence, this study applies this hybrid method to determine the important attributes under
linguistic preferences. This study contributes to the literature by distinguishing the critical SKM
attributes under linguistic preferences and emphasizes the attributes that must be enhanced in the
agribusiness industry. The following questions are raised:
•What are the decisive attributes of SKM under linguistic preferences?
•What attributes must be enhanced in the circular agribusiness industry?
The agribusiness industry in Vietnam has exhibited challenging characteristics that are highly
volatile in terms of both production and market conditions, especially waste. In addition, SKM
has been relatively underexplored in agribusiness, in which SKM has only been examined from
perspectives such as innovation, supply chains or risk management [
1
,
16
]. Fabbe-Costes et al. [
17
]
emphasized that firms must work together in closed-loop SSCM, which has also been noted not only
from a social perspective but also from environmental and economic perspectives (circular system).
The present study consists of five parts. The next section provides a review and discussion of the related
literature on KM and SKM. Then, the paper describes the industry background and the proposed
methods. The fourth section presents the industry background and analytical results. After the
results are discussed, theoretical and managerial implications are described. Concluding remarks and
suggestions for potential future studies are included in the final section.
Sustainability 2018,10, 2389 3 of 20
2. Literature Review
This section introduces the theoretical debates about KM, SKM, and closed-loop SSCM and
discusses proposed methods and measures.
2.1. Knowledge Management
KM relates to even the most basic work processes of firms. Typically, Hernaus and Mikuli´c [
18
]
found that even with regard to the existence and importance of the interaction between work
characteristics and work outcomes, only the knowledge characteristics of work design had
a significant effect on both dimensions of work behavior. From the opposite side of the spectrum,
Aziz and Rizkallah [19]
measured the relationship between idea generation by employees and the
effects of certain attributes on innovation performance. The authors found that while many of the
attributes were significantly correlated with employees’ innovative idea generation, functional and
motivational attributes appeared to be the most important. Tseng [
4
] proposed KM capabilities as
important activities that help firms continuously improve their environments because of the great
emphasis on green product development in competitive and sustainable markets. Bloodgood [
20
]
referred to KM as the creation, storage and utilization of routines. Therefore, these KM approaches
raise concerns about the creation, storage, dissemination, and application of firm knowledge within
supply chain networks.
López-Nicolás and Meroño-Cerdán [
6
] argued that despite considerable advances in certain
aspects, heterogeneous and confounding results had been presented related to the variables affecting
KM programs. Duhon [
21
] defined KM as an integrated approach to identify, capture, evaluate,
retrieve, and share all information, and this information can take the form of databases, documents,
policies, procedures, or formerly uncaptured expertise and experience among individual workers.
KM was also observed as a process of creating, acquiring and transferring knowledge reflected in the
behavior of the firm [
22
]. A KM framework should incorporate a basic understanding of knowledge
operations and infrastructures to support operations. According to Lee and Choi, KM enablers are
mechanisms that are employed by firms to foster consistent knowledge usage [
23
]. KM refers to
identifying and leveraging the collective knowledge in a firm [
24
], but there is still a problem regarding
the integration of such knowledge into a firm’s strategic levels. The main difficulties for firms regarding
sustainable development include the failure of KM due to the difficulty of integrating the knowledge
at the strategic level [
7
]; hence, the ability of firms to identify, codify and leverage/use their knowledge
sources as a firm strategy to increase overall performance and competitive advantages is limited.
Hence, SKM needs to be formulated as a set of measures at a firm’s strategic level.
2.2. Strategic Knowledge Management
SKM can be thought of as the strategically codifying and personalizing aspects of knowledge
(explicit and tacit) about an organization that increase overall performance [
8
]. Venkitachalam and
Willmott [
10
] showed the importance of executives’ responsibility to emphasize the codification of
knowledge instead of fixating on strategic knowledge. Hansen et al. [
25
] debated distinguishing
between personalization and codification. In codification, knowledge is extracted from the person
who developed it before being made independent of that person and reused for various purposes,
whereas personalization focuses on dialogue between individuals [
6
]. In the relevant literature,
arguments related to the personalization and codification of knowledge are easily understood
by academics and practitioners [
6
]. Furthermore, Venkitachalam and Willmott [
10
] indicated the
importance of emphasizing the equivalence (or congruence) between aspects of codification and
personalization to maintain work productivity and innovation capacity.
Firms must utilize a global and consistent vision when managing knowledge and selecting the
tools to be implemented [
6
]. A firm’s innovation process depends greatly on SKM [
26
], especially
tacitly. Darroch [
27
] provided empirical evidence to support the view that a firm with SKM ability
Sustainability 2018,10, 2389 4 of 20
is also likely to be more innovative. Massey et al. [
28
] reported evidence from a real firm that
implemented and achieved improvements in the innovation process and performance. Generally,
innovations in firms emerge from knowledge creation and sharing using the strong social networks
that a personalization strategy seeks to foster [
6
]. A networking strategy thus supports most consulting
firms’ approaches to SKM in a sophisticated way, resulting in an overwhelming amount of codified
knowledge. Venkitachalam and Willmott [
8
] argued that overemphasized codification efforts can result
in structuration, and through this process, dilute the purpose, meaning and contextual relevance of
knowledge work. This study seeks to identify the decisive attributes that can increase performance in
the circular agribusiness industry to overcome the gap between academics and practitioners.
2.3. Closed-Loop SSCM
Kirchherr et al. [
29
] described the closed loop as an economic system based on business
models that replace the “end of life” by reducing, reusing, recycling and recovering materials in the
manufacturing/distribution process. Closed-loop systems operate at the micro level (firms, consumers,
and products), the meso level (eco-industrial parks) and the macro level (city, region, country and
world) to achieve sustainable development, including the creation of environmental quality, economic
prosperity and social equity for the benefit of present and future generations. Shankar et al. [
30
] referred
to the closed-loop strategic supply chain as one of the most prudent approaches to sustainability, as it
supports continuity and long-term firm survival. Tsao [
31
] argued that closed-loop SSCM considers
both the transition and reverse supply chain concurrently. SSCM considers new product streams to
minimize distribution costs, whereas reverse logistics involves returning product streams to minimize
product recovery costs.
Hosoda and Disney [32] argued that understanding the functions of closed-loop SSCM can help
improve firm efficiency and economic viability. Closed-loop SSCM has also proven to be a fruitful and
important issue for improving sustainability [33]. However, the complexity of closed-loop SSCM has
not been adequately addressed due to difficulties in implementation and strategic integration [
34
].
Recently, SSCM has been widely studied by both academics and industrial practitioners [
14
,
35
,
36
].
Many studies have also discussed important SSCM attributes [
37
,
38
], but little is known regarding
how SKM can bridge closed-loop SSCM. In addition, prior studies have not included SKM as a critical
attribute in closed-loop SSCM in the circular agribusiness industry. Therefore, it is necessary to identify
the attributes of closed-loop SSCM in the industry.
2.4. Proposed Methods
López-Nicolás and Meroño-Cerdán [
6
] used a survey method to elucidate the consequences of
SKM with regard to the improvement and effectiveness of firms. Venkitachalam and Willmott [
10
]
adopted a qualitative multiple case-study method to identify the attributes that shape the development
context of KM. Tseng [
39
] proposed an analytic network process, and a second survey was developed
to apply decision-making trials and evaluation laboratories as the most appropriate tools to understand
hierarchical relationships and cause and effect in firm environmental KM under conditions of
uncertainty. Aliewi et al. [
40
] used the multi-criteria decision analysis methodology to clarify the
issue of strategic management. Tseng et al. [
13
] demonstrated how sustainable service supply chain
management under uncertainty could contribute to the sustainability debate using the fuzzy Delphi
method to screen alternative attributes in the industry. However, there has been a lack of studies using
multiple decision-making methods to clarify SKM through sets of measures.
Patil and Kant [
41
] proposed TOPSIS to identify and rank solutions utilizing KM in supply
chains. KM is very important for the management of firm knowledge. To support the evaluation
and selection of KM from a user perspective, a multi-criteria decision-making standard incorporates
the implementation of quality functions with preference techniques. Ordered by analogy to an ideal
solution, TOPSIS in an intuitive blurring environment was proposed [
42
]. Decision-making issues are
subject to difficulties, such as unclear goals and consequences. The TOPSIS method is a practical and
Sustainability 2018,10, 2389 5 of 20
useful technique for ranking and selecting external alternatives through distance measures, which are
used to compare alternatives [
42
]. This proposed method provides a decision support tool that is
accurate, effective and systematic for the implementation of an SKM application. Hence, this study
uses the TOPSIS method to clarify SKM and the contribution of closed-loop SSCM.
2.5. Proposed Measures
The existing literature has proposed several attributes. These attributes include four aspects and
twenty-two criteria, as listed in Table 1. The four aspects include top management support, the KM
process cycle, KM performance, and firm performance.
Table 1.
Aspects and criteria. KM: knowledge management, SKM: strategic knowledge management.
Aspects (AS) Criteria References
AS1 Top management support
C1 Systematic knowledge
López-Nicolás and
Meroño-Cerdán [
6
],
Ng et al. [43]
C2 Advanced knowledge acquisition
C3
Archived knowledge from projects and meetings
C4 Codified knowledge sharing
C5 Knowledge sharing from informal dialogue
and meetings
C6 SKM
AS2 KM process cycle
C7 Knowledge creation/capture
Huang et al. [44]
C8 Knowledge storage
C9 Knowledge transfer/sharing
C10 Knowledge application/use
AS3 KM performance
C11 Organizational problem solving
Kim et al. [45]
C12 Communication improvement
C13 Employee abilities development
C14 Value enhancement
C15 Customer satisfaction
AS4 Firm performance
C16 Firm learning and growth
López-Nicolás and
Meroño-Cerdán [6]
C17 Firm profit
C18 High-quality products/services
C19 Efficient resource use
C20 Quality-oriented internal processes
C21 Qualified employees
C22 Creative and innovative employees
Top management support is considered one of the critical attributes that influences
organizational knowledge [
46
]. Systematic knowledge (C1) is related to know-how, technical skill,
and problem-solving methods that allow knowledge to be shared quickly and with wider access [
6
].
Advanced knowledge acquisition (C2) is another knowledge resource through which firms acquire
information through formal documents and manuals as well as from experts or coworkers. In addition,
archived knowledge from projects and meetings (C3) involves the use of documents, including
the results of the projects and firm meetings [
6
], thus contributing to the synthesis of knowledge
to support decisions in firms. Codified knowledge sharing (C4) through codified forms, such as
manuals or documents, supports the development of innovation. Knowledge sharing through
informal dialogue and meetings (C5) is an important aspect of top management support and can
help management understand the effects of embedded knowledge and facilitate the transfer of such
knowledge [
6
]. Ng et al. [
43
] discussed SKM (C6) and categorized it into codification, personalization
and integrated strategies.
Many prior studies have argued that any organization implementing KM will enter the KM
process cycle [
47
]. The initiation of the KM process cycle involves either the creation or acquisition of
knowledge by an organization. Knowledge creation/capture (C7) creates a virtual space with both
explicit and tacit knowledge [
44
]. Knowledge storage is considered one of the phases of a typical KM
process cycle [
44
,
48
]. Knowledge storage (C8) includes workflow tools, databases and search engines
for arranging system knowledge in a firm. The rapid changes in technology and international business
have encouraged knowledge sharing. Knowledge transfer/sharing (C9) is related to cross-functional
Sustainability 2018,10, 2389 6 of 20
collaboration and teamwork [
44
]. Furthermore, knowledge sharing to enhance or develop information
or create new knowledge facilitates the development of innovation [6].
Darroch [
27
] stated that during the knowledge application/use phase, SKM taps into the firm’s
power, combining humans with a technological nexus. Knowledge application/use (C10) includes
interpreting and combining knowledge in human networks. In addition, the performance of knowledge
management relies on organizational problem solving (C11) to seek innovation opportunities.
Additional criteria include communication improvement (C12) employee ability development (C13)
and value enhancement (C14), which includes enhancing the values of products and services and
customer satisfaction (C15).
The present study suggests that the impact of a KM strategy on firm performance can be better
understood by analyzing different dimensions of firm performance [
6
]. Firm learning and growth
(C16) is a key criterion for the rapid growth of firms. Additional key criteria include firm profit (C17),
high-quality products/services (C18), efficient resource use (C19), quality-oriented internal processes
(C20), qualified employees (C21), and creative and innovative employees (C22).
3. Methods
Fuzzy TOPSIS was proposed by Hwang and Yoon [
49
] and is the best-known technique for
solving decision-making problems. This approach is based on the notion that the optimal path should
be the shortest distance to the ideal positive solution (the solution that minimizes cost norms and
maximizes welfare criteria), while the path with the greatest distance is the least optimal solution.
Fuzzy sets can be used to formally define the fuzzy intersection, union and fuzzy subset. Based on
these concepts, fuzzy theory was subsequently developed [
14
,
15
]. Fuzzy set theory is the only
means to quantify fuzzy sets and enable a precise mathematical method for analyzing and processing
uncertainties or linguistic preferences. The triangular fuzzy number (TFN) was employed to fuzzify
the meaning of expert cognition value.
a
represents the lower limit of the original cognition value,
b
is the median of the original cognition value, and
c
is the upper limit of the original cognition value.
Linguistic scales that depict the various levels of importance and performance are presented. The TFNs
are as indicated [15].
Proposed Analytical Procedures for Fuzzy TOPSIS
1.
Create an evaluation matrix consisting of
m
alternatives and
n
criteria, with the intersection of
each alternative and criterion given as xij; we therefore have a matrix (xij )mx n.
2.
Based on the evaluation matrix, develop an assessment questionnaire. Once the responses are
returned, the responses are transformed into fuzzy numbers in the following sub-steps. If the
kexperts in the decision group need to consider the fuzzy weight
Fk
ij =ak
ij ,bk
ij ,ck
ij
of the ith
criterion, the jth criterion appreciated by the kth evaluators is affected. This study also uses expert
weights as parameters for each respondent. The equations are set forth below. Normalization:
Fak
ij =lk
1ij −min lk
ij )
∆max
min
Fbk
ij =mk
ij −min mk
ij
∆max
min
Fck
ij =uk
ij −min uk
ij
∆max
min
where ∆max
min =max ck
ij −min ak
ij
(1)
Sustainability 2018,10, 2389 7 of 20
Calculate left (L) and right (R) normalized values:
NLs =fbk
ij
1+fbk
ij −fak
ij
NRs =fck
ij
1+fck
ij −fbk
ij
(2)
Compute total normalized crisp value:
ncvk
ij =hfLs (1−fLs )+(fRs )2i
[1−fLs +fRs ](3)
Aggregation of crisp values. The aggregate value of the subjective judgments from the composite
opinions of kevaluators:
Ak
ij =ncv1
ij +ncv2
ij +ncv3
ij +· · · ncvk
ij
k(4)
3. Calculate the weighted normalized decision matrix
V= (vij )mxn = (wjvij)mxn,i=1, 2, · · · m
where
wij =WD×wi
∑n
j=1wi
,
j=
1, 2,
· · · n
, such that
∑n
j=1wi=
1, and
wi
is the original weight
given to the indicator
wj
,
j=
1, 2,
· · · n
.
WD
represents the interrelationships among the attributes.
4. Determine the worst alternative ASwand the best alternative ASb:
ASb=v+
1,v+
2, . . . v+
n=maxjvij
i∈I),minjvij
i∈j
ASw=v−
1=v−
2, . . . v−
n=minjvi j
i∈I),maxjvij
i∈j,
where Iis associated with the benefit criteria, and jis associated with the cost criteria.
5. Calculate the separation measure between the target alternative and the best alternative:
dib =r∑n
j=1(vij −v+
j)2,i=1, 2, · · · m(5)
and the distance between the target alternative and the worst alternative:
diw = = r∑n
j=1(vij −v−
j)2,i=1, 2, · · · m(6)
6. Calculate the similarity to the worst condition:
Siw =diw
(diw +dib ), 0 ≤Siw ≤1, i=1, 2, · · · m(7)
Siw =
1 if and only if the alternative solution has the best condition, and
Siw =
0 if and only if the
alternative solution has the worst condition.
7. Rank the alternatives according to Siw (i=1, 2, · · · m).
Sustainability 2018,10, 2389 8 of 20
4. Results
This section includes the industry background and the analytical results of the proposed
hybrid approaches.
4.1. Industry Background
Vietnam provides an appropriate context for conducting a study of the positional advantages in
agribusiness [
50
]. Competition exists between state-owned companies and private firms, and between
local products and imported products. Vietnam provides new opportunities for agriculture exporters
and challenges for commitments that previously only flourished in the domestic market. The industry
is operating at a steady pace, and production is planned. Vietnam has shifted from a centrally
planned economy to a market economy, with open market trade policies and increased competitive
pressures. There is great opportunity for agribusiness. However, there is also a need to understand
the closed-loop SSCM due to the environmental requirements, as well as improve the industry’s
competitive advantages to work toward sustainability.
The agribusiness industry exhibits challenging characteristics, particularly high volatility, in terms
of both production and market conditions. In addition, Vietnam’s agribusiness industry generally
neglects the importance of closed-loop SSCM. The literature indicates that SKM needs to be studied in
closed-loop SSCM. In addition, prior studies have revealed the relationships among source, location
advantage and business efficiency in agricultural production, but there has been a lack of SKM
integrated into a framework at firms’ strategic levels in the industry. The agribusiness industry suffers
from high costs and resource waste. Thus, this study utilizes an expert decision-making method
to understand the main performance attributes in order to reduce costs and enhance the industry’s
performance. The respondents are four individuals from industrial sectors and three individuals from
academic institutes with at least 10 years of work and research experience in the industry, all of who
understand how agribusiness can work toward sustainability. The four individuals from the industry
were selected from focal Vietnamese agribusinesses and were top managers, which enabled them to
provide appropriate assessments.
4.2. Analytical Results
Step 1: Transforming correspondence into fuzzy scales
Based on the steps in fuzzy TOPSIS, correspondence can be acquired after experts’ assessments.
However, this correspondence possesses qualitative features that must be transformed into quantitative
values for further computation. Table 2presents the transformation results from the experts.
For example, if an assessment is stated to be U, it means that the criterion is unimportant, and the
transformation result is stated as [0.0 0.1 0.3], as shown in Table 3.
Table 2. The Fuzzy Triangular Numbers for Transformation.
Linguistic Preference Fuzzy Triangular Numbers
a b c
Unimportant (U) 0.0 0.1 0.3
Less Important (LI) 0.1 0.3 0.5
Important (I) 0.3 0.5 0.7
Moderately Important (MI) 0.5 0.7 0.9
Very Important (VI) 0.7 0.9 1.0
Sustainability 2018,10, 2389 9 of 20
Table 3. Transformation results from the experts.
From Academic Institutions From Industrial Experts
Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7
C1 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0 0.7 0.9 1.0 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0
C2 0.5 0.7 0.9 0.7 0.9 1.0 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0 0.7 0.9 1.0 0.5 0.7 0.9
C3 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9
C4 0.7 0.9 1.0 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9
C5 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.7 0.9 1.0 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9
C6 0.7 0.9 1.0 0.5 0.7 0.9 0.7 0.9 1.0 0.7 0.9 1.0 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9
C7 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9
C8 0.5 0.7 0.9 0.5 0.7 0.9 0.7 0.9 1.0 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9
C9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9
C10 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9
C11 0.5 0.7 0.9 0.7 0.9 1.0 0.3 0.5 0.7 0.3 0.5 0.7 0.7 0.9 1.0 0.3 0.5 0.7 0.5 0.7 0.9
C12 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9
C13 0.3 0.5 0.7 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9
C14 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.7 0.9 1.0
C15 0.5 0.7 0.9 0.7 0.9 1.0 0.3 0.5 0.7 0.7 0.9 1.0 0.7 0.9 1.0 0.5 0.7 0.9 0.5 0.7 0.9
C16 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.5 0.7 0.9 0.7 0.9 1.0 0.5 0.7 0.9 0.5 0.7 0.9
C17 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9
C18 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9
C19 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7
C20 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.7 0.9 1.0
C21 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7
C22 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9 0.3 0.5 0.7 0.5 0.7 0.9
Sustainability 2018,10, 2389 10 of 20
Step 2: Ranking the criteria
In step 2, the fuzzy numbers are transformed into crisp values. Subsequently, in step 3, the weight
of each criterion
vij
is acquired. Table 4presents the best alternative (
ASb
) and important weight
rankings to provide a comparative basis for acquiring the top three criteria.
Table 4. Fuzzy importance weights and rankings.
Criterion vij ASbRanking
C1 0.643 0.843 0.971 0.819 1
C2 0.614 0.814 0.957 0.795 2
C3 0.586 0.786 0.943 0.771 3
C4 0.443 0.643 0.829 0.638 16
C5 0.500 0.700 0.886 0.695 9
C6 0.529 0.729 0.886 0.714 7
C7 0.471 0.671 0.857 0.667 11
C8 0.500 0.700 0.886 0.695 8
C9 0.414 0.614 0.814 0.614 17
C10 0.414 0.614 0.814 0.614 19
C11 0.471 0.671 0.843 0.662 13
C12 0.443 0.643 0.843 0.643 14
C13 0.529 0.729 0.900 0.719 6
C14 0.414 0.614 0.800 0.610 22
C15 0.557 0.757 0.914 0.743 5
C16 0.557 0.757 0.929 0.748 4
C17 0.471 0.671 0.871 0.671 10
C18 0.443 0.643 0.843 0.643 15
C19 0.414 0.614 0.814 0.614 19
C20 0.471 0.671 0.857 0.667 11
C21 0.414 0.614 0.814 0.614 19
C22 0.414 0.614 0.814 0.614 17
Next, the rest of step 3 is focused on gathering the
WD
. To enhance the reliability of this
study, a decision-making trial and evaluation laboratory approach was adopted to rank the criteria,
and TOPSIS is used to confirm the results by comparing the ranking of the criteria in Tables 4and 5.
The purpose of this process is to enhance the accuracy of the decision-making. Thus, the results reveal
that the top three most highly weighted criteria are systematic knowledge (C1), advanced knowledge
acquisition (C2) and archived knowledge from projects and meetings (C3).
Table 5. Criteria Ranking.
WDAverage Fuzzy Importance Weight DEMATEL Best
Non-Fuzzy Performance Normalized Weights DEMATEL-Ranking
C1 0.210 0.021 0.135 0.177 0.204 0.172 0.0547 1
C2 0.640 0.063 0.129 0.171 0.201 0.167 0.0531 2
C3 0.180 0.018 0.123 0.165 0.198 0.162 0.0515 3
C4 0.950 0.093 0.093 0.135 0.174 0.134 0.0426 16
C5 0.150 0.015 0.105 0.147 0.186 0.146 0.0464 8
C6 0.650 0.064 0.111 0.153 0.186 0.150 0.0477 7
C7 0.190 0.019 0.099 0.141 0.180 0.140 0.0445 11
C8 0.970 0.095 0.105 0.147 0.186 0.146 0.0464 8
C9 0.900 0.088 0.087 0.129 0.171 0.129 0.0410 17
C10 0.420 0.041 0.087 0.129 0.171 0.129 0.0410 19
C11 0.350 0.034 0.099 0.141 0.177 0.139 0.0442 13
C12 0.330 0.032 0.093 0.135 0.177 0.135 0.0429 14
C13 0.450 0.044 0.111 0.153 0.189 0.151 0.0480 6
C14 0.390 0.038 0.087 0.129 0.168 0.128 0.0407 22
C15 0.250 0.025 0.117 0.159 0.192 0.156 0.0496 5
C16 0.620 0.061 0.117 0.159 0.195 0.157 0.0499 4
C17 0.290 0.028 0.099 0.141 0.183 0.141 0.0448 10
C18 0.770 0.075 0.093 0.135 0.177 0.135 0.0429 15
C19 0.240 0.024 0.087 0.129 0.171 0.129 0.0410 19
C20 0.230 0.023 0.099 0.141 0.180 0.140 0.0445 12
C21 0.150 0.015 0.087 0.129 0.171 0.129 0.0410 19
C22 0.870 0.085 0.087 0.129 0.171 0.129 0.0410 17
Sustainability 2018,10, 2389 11 of 20
Step 3: Reassessment after aspect consideration
The experts were asked to assess the effects of the aspects under each criterion. The assessments
are still stated as linguistic preferences that need to be transformed into an assessment scale, as shown
in Table 6. The assessment of criteria under aspects presents the final transformation of the seven
experts, e.g., expert 1 expressed VI for C1 under AS1, and this linguistic preference is transformed into
h7 9 10 i(as shown in Table A1).
Table 6. Linguistic scale for aspects and criteria.
Linguistic Preference Assessment Scales (Aspects)
Unimportant (U) 0 1 3
Less Important (LI) 1 3 5
Important (I) 3 5 7
Moderately Important (MI) 5 7 9
Very Important (VI) 7 9 10
Step 4: Generating the fuzzy-weighted normalized decision matrix
Next, these assessments need to be combined into an integrated decision matrix through the
procedure employed in step 2 (presented in Table A2). Then, we repeat step 3 to obtain the normalized
decision matrix in Table A3. Therein, the normalized values are computed as 5.000
/
8.857
=
0.565,
as displayed in gray color. Subsequently, each aspect has its own original weight that needs to be
associated with the normalized decision matrix to obtain the weighted normalized decision matrix,
as shown in the weighted normalized decision matrix (as Table A4).
Step 5: Ranking the aspects
Steps 4 through 7 are followed to obtain the
Siw
for each aspect. Therein,
Siw
for AS1 is calculated
as 166.852
/(93.347 +166.852)=
0.641. Based on the values of
Siw
, the ranking of the aspects is
determined, as shown in Table 7. Furthermore, the results show that the top two aspects are top
management support (AS1) and the KM process cycle (AS2).
Table 7. Ranking the aspects.
Aspect dib diw Siw Ranking
AS1 93.347 166.852 0.641 1
AS2 85.058 155.405 0.646 3
AS3 84.486 154.678 0.647 4
AS4 90.904 163.637 0.643 2
5. Implications
This section presents the theoretical and managerial contributions.
5.1. Theoretical Implications
The results confirmed that top management support (AS1) is the decisive attribute of SKM.
Top management support comprises knowledge sharing and systematic knowledge in order to use
knowledge effectively to develop an approach. Knowledge must be formatted in such a manner that
allows it to circulate and be exchanged, and archiving documentation at the completion of a project is
the primary method of knowledge retention and transfer. Top management support is the basis for
facilitating communication among supply chain partners, and value creation is a vital element for skill
development. Moreover, top management support is important for creating and maintaining positive
Sustainability 2018,10, 2389 12 of 20
knowledge in a firm. Thus, enhancing top management support is viewed as an important means of
improving SSCM.
The growing importance of knowledge has encouraged managers to pay greater attention to
firms’ SKM. SKM is always linked with firm activities, such as improved quality of products/services,
employee training, or the efficient use of resources.
Prior studies have reported significant relationships between firm performance and SKM.
However, these studies have not provided clarity regarding the real impact of SKM on firm
performance [
6
]. Firms tend to support competence building through learning and interacting,
thus enhancing the ability to pursue product or service innovations. Knowledge is both a crucial input
and a crucial output of innovation processes [
51
]. Positive firm performance requires considerable
effort directed toward training, improving the quality of products/services and encouraging innovative
employees. Thus, firms can attain new skills, techniques, and information from outside firms.
In addition, knowledge improves a firm’s performance and competitiveness and ensures the
synchronous development of certain aspects of the firm, such as enhancing the value of its products
and services and contributing to the development of employees’ abilities.
The KM process cycle (AS2) systematically shows how information is transformed into
knowledge via creation and application processes [
44
]. Through this process, knowledge embodied
in human networks, knowledge creation, and storage applications enable effective problem solving,
decision-making and innovation. The gathering of knowledge builds on the process of finding and
synthesizing external knowledge for operations related to the firm’s context to upgrade the knowledge
level of the firm [
52
]. Knowledge accumulated through databases and firm learning has become a basis
for the core competence of today’s firms. Moreover, the KM process cycle generates quality and useful
information to benefit a range of firm activities. Hence, the KM process cycle has become one of the
key means by which SKM helps firms survive and succeed in a highly competitive environment.
In conclusion, this study contributes to the field of KM by exploring important attributes of SKM
and providing deeper insights for SKM research. This study provides evidence suggesting that top
management support, firm performance and KM process cycles are the most important attributes of
SKM. Therefore, greater effort should be directed toward fostering these three attributes to achieve
efficient SKM. In addition, the combination of data, information technology and the innovative capacity
of people contributes to innovation, improved performance and competitiveness.
5.2. Managerial Implications
This study aims to address the lack of evidence about SKM in closed-loop SSCM. This study also
provides suggestions for firms to improve SKM performance and consequently firm performance.
Few studies have identified the SKM attributes and the impact of the key attributes in the agribusiness
industry. The results of this study indicate that the five most important SKM-related criteria
are systematic knowledge (C1), advanced knowledge acquisition (C2), archived knowledge from
projects and meetings (C3), firm learning and growth (C16), and customer satisfaction (C15).
These five criteria reinforce the importance of the basic attributes of closed-loop SSCM. Therefore,
these top-ranked criteria provide the focal points for practices in operational activities in order to
achieve better performance.
Systematic knowledge (C1) is the most important criterion related to improving SKM. Systematic
knowledge includes basic knowledge, planning knowledge, and analysis and design knowledge.
Systematic knowledge is the result of learning a system through studying it or acquiring experience
through the firm’s activities and the relationship between them. This criterion also relates to the
interaction between knowledge and systematics to obtain a clear understanding of market trends for
long-term development in addition to the exchange of technology to create new products and services
that suit the requirements of the market. Thus, agribusiness firms are encouraged to exert effort to create
common knowledge, such as through outsourcing, product innovation and collaborative research.
Therefore, managers must pay closer attention to systematic knowledge in SKM and competitors
Sustainability 2018,10, 2389 13 of 20
in order to achieve closed-loop SSCM. Advanced knowledge acquisition (C2) constitutes a higher
level of knowledge system that extends the vision of resources and knowledge to the industry and
leads to competitive advantage. Advanced knowledge in specialized areas requires management to
determine how to develop questions for study and specific methods for firms. These attributes provide
an effective means to transfer new knowledge and technical skills to firms so that they can adapt
to market changes and customer needs that benefit the firms in terms of faster production, reduced
production and logistics costs, improved efficiency, and maximized return on investment. Firms in this
industry are recommended to encourage the creation of common knowledge, such as software, product
innovation and collaborative research. Therefore, managers should pay more attention to SKM and
their competitors in order to determine how to improve performance via SKM in closed-loop SSCM.
Archived knowledge from projects and meetings (C3) is a method to improve SKM. Firms can
document and record the lessons learned from projects as a means to communicate what should or
should not be done in the future. This process begins by incorporating the lessons learned at the end of
each project into a database. This practice is greatly beneficial for firms: it enables continuous learning
and avoids repetitive mistakes. To maintain projects, knowledge can be transferred through lessons
learned for future study. Learning from errors is an essential issue in closed-loop SSCM. Firms need to
consider placing greater emphasis on archived knowledge for capacity building, especially in efforts
to improve SKM. A firm’s learning and growth (C16) can be observed as important parts of building
SKM. The two attributes that relate to sustained growth are the functions of size and age: the expected
growth rate of a firm decreases with age and depends on its size. The third part relates to the normative
nature of the criteria. A reallocation of resources is required in the economy that can lead to improved
social welfare versus a decentralized equilibrium. A firm may make decisions that result in low sales
or it can exit the market, especially if it is unsure about its true needs, rather than remaining and
recording low growth. Hence, effective SKM has benefits for closed-loop SSCM with regard to firm
learning and growth.
Customer satisfaction (C15) is a commonly used term in marketing and is a measure of
whether the products and services provided by firms meet or exceed customer expectations [
53
].
Customer satisfaction is represented by the number of clients or the percentage of the total clients
who report to a firm that its products or services exceed a satisfaction goal. Customer satisfaction
also provides marketers and firm owners with a metric that they can use to manage and improve
closed-loop SSCM. Customer satisfaction is the best indicator of whether customers will make future
purchases. Asking customers to rate their satisfaction on a scale is a good method to determine whether
they will be repeat customers or even promoters [
54
]. This approach can provide a useful instrument
for a firm operating under SKM.
These findings confirm that SKM relies on a central role to facilitate the integration of resources
directly and to lead an industry in making improvements. Furthermore, the results provide a path to
closed-loop SSCM, which can enable these firms to successfully exploit development opportunities
through the development of knowledge systems, and in turn, develop new products and services
before competitors and thus improve their performance.
6. Conclusions
SKM has received increased attention in recent years; however, the current literature is lacking
in terms of guidance with regard to integrating SKM in closed-loop supply chain management,
specifically in the area of agribusiness. This study reveals that the decisive attribute for SKM
is top management support. Thus, the circular agribusiness industry needs to improve its top
management support and take systematic knowledge, advanced knowledge acquisition and archived
knowledge from projects and meetings into account. These criteria enable a firm to enhance its
competitive advantage and performance. Moreover, this study includes a set of measures and applies
fuzzy TOPSIS with interrelationship weights, which is a practical and useful technique for ranking,
selecting and comparing a solution through the proposed SKM measures. The proposed aspects and
Sustainability 2018,10, 2389 14 of 20
criteria have been ranked by experienced experts. Top management support, KM process cycles,
KM performance and firm performance are the four main aspects, and these are quantified using
22 criteria that can be measured in the industry. The results show that systematic knowledge, advanced
knowledge acquisition, archived knowledge from projects and meetings, firm learning and growth,
and customer satisfaction are the top five criteria that support the set of measures and contribute to
the industry’s performance. The findings indicate that top management support, firm performance
and the KM process cycle must receive higher priority than other aspects of management decision
making. There is a significant gap in terms of operational SKM processes in firms and the degree
to which top management creates and maintains positive knowledge about firms’ operations in
the agribusiness industry. Firm performance supports competence building through learning and
interacting, thus enhancing the ability to pursue product or service innovation. The KM process cycle
generates quality and useful information to benefit a range of firm activities.
The results regarding the consequences of the KM process cycle support prior studies by filling
in gaps in terms of interrelationships and key successful attributes. The SKM process cycle must be
improved and successfully implemented within firm strategies to enhance SKM performance and
firm performance in closed-loop SSCM. Systematic knowledge, advanced knowledge acquisition,
and archived knowledge from projects and meetings help firms to improve management skills
and build the right strategy in the agribusiness industry. Firm learning and growth and customer
satisfaction are instruments that can help a closed-loop SSCM operate. Finally, the set of measures for
conceptual frameworks and limitations is essential for promoting the use of SKM. First, the set
of attributes might not be comprehensive, and future studies should provide a more extensive
examination of the SKM context. The sample collection was based only on the agribusiness industry
in Vietnam; therefore, the generalizability of the findings is limited. Future studies should focus on
multiple countries or industries to broaden the results. Finally, this study uses the fuzzy TOPSIS
method; thus, future studies using other methods could have different results. In addition, the small
sample size is another limitation of this study, as assessments must be made twice, and there are many
items on the questionnaire. This might reduce the consistency of the study. Thus, decreasing the
number of items on the questionnaire is one the major barriers that needs to be overcome in future
studies. In addition, future research could also redesign the assessment procedure to make only one
assessment to enhance the consistency. Although it has the above limitations, this paper still offers
a precise guideline for the circular agribusiness industry to take SKM into account.
Author Contributions:
D.-H.S. and C.-M.L. investigated the Vietnamese textile industry. C.-H.L. and Y.-J.M.P.
contacted the students to collect the data and implemented the transformation. K.-J.W. and M.-L.T. drafted the
study and improved the method to guarantee its reliability and validity.
Funding:
This research was funded by [National Natural Science Foundation of China] grant number [71701029],
[Liaoning Academy of Social Sciences Fund] grant number [L17BGL019] and [Fundamental Research Funds for
the Central Universities] grant number [DUT18RC(4)002].
Acknowledgments:
For the data collection, we would like to thank the following team: Dang Thi Oanh Kieu,
Nguyen Thanh Vinh, Le Thi Thanh Ha Meo, Nguyen Kim Hang, Nguyen Hoang Kim Minh and Le Quoc Minh.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2018,10, 2389 15 of 20
Appendix A.
Table A1. Assessment of criteria under aspects.
Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7
C1
AS1
7 9
10
57935757957935779
10
AS2
7 9
10
57913535779
10
7 9
10
5 7 9
AS3
7 9
10
7 9
10
57979
10
57979
10
5 7 9
AS4
35757957979
10
35779
10
7 9
10
C2
AS1
13579
10
57913579
10
7 9
10
1 3 5
AS2
35735779
10
57979
10
135579
AS3
135357135579357357357
AS4
357357579357579357135
C3
AS1
57979
10
13579
10
57979
10
5 7 9
AS2
35713513579
10
357135579
AS3
7 9
10
579135579135357357
AS4
7 9
10
135357579579357357
C4
AS1
35735713557979
10
357579
AS2
57957979
10
35779
10
135135
AS3
13557913513513557979
10
AS4
13513535779
10
57979
10
1 3 5
C5
AS1
579135579357579135579
AS2
579579135579579579135
AS3
7 9
10
13557935735757979
10
AS4
7 9
10
57935757957957979
10
C6
AS1
7 9
10
357357135135357579
AS2
7 9
10
7 9
10
579135357579135
AS3
57957935779
10
7 9
10
35779
10
AS4
57957979
10
57979
10
579135
C7
AS1
7 9
10
57935713579
10
7 9
10
3 5 7
AS2
35779
10
13579
10
135579135
AS3
13579
10
35779
10
57979
10
5 7 9
AS4
57957957979
10
13557979
10
C8
AS1
35757935779
10
35779
10
1 3 5
AS2
57957979
10
7 9
10
7 9
10
357579
AS3
35713513579
10
579357579
AS4
357135135357135579579
C9
AS1
35713579
10
135357579579
AS2
57935757913557913579
10
AS3
7 9
10
13535735713535779
10
AS4
57957935779
10
7 9
10
357135
C10 AS1
57957979
10
7 9
10
357357579
AS2
7 9
10
7 9
10
13579
10
135135579
AS3
13579
10
7 9
10
357579135135
AS4
7 9
10
7 9
10
357357579357135
C11 AS1
579579579135357579357
AS2
13513579
10
57935779
10
1 3 5
AS3
135579357579357135357
AS4
7 9
10
57935713579
10
35779
10
C12 AS1
57957935713579
10
579579
AS2
13557979
10
13579
10
357357
AS3
7 9
10
13557935757979
10
1 3 5
AS4
13535713579
10
135579579
C13 AS1
57957979
10
35713579
10
3 5 7
AS2
13579
10
35735779
10
357135
AS3
13513535779
10
7 9
10
135579
AS4
57935735779
10
7 9
10
135357
C14 AS1
35779
10
57913535779
10
7 9
10
AS2
7 9
10
7 9
10
7 9
10
7 9
10
357357357
AS3
35757979
10
7 9
10
13579
10
5 7 9
AS4
57935713579
10
7 9
10
135579
Sustainability 2018,10, 2389 16 of 20
Table A1. Cont.
Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7
C15 AS1
57979
10
13513513579
10
1 3 5
AS2
7 9
10
13557979
10
7 9
10
35779
10
AS3
35779
10
7 9
10
57957935779
10
AS4
13557979
10
57957913579
10
C16 AS1
13557935713513579
10
1 3 5
AS2
357357357357357579135
AS3
35757913535735779
10
1 3 5
AS4
7 9
10
35779
10
35779
10
135357
C17 AS1
135357357135135579357
AS2
35713579
10
35779
10
7 9
10
3 5 7
AS3
7 9
10
35713535779
10
579579
AS4
57957913579
10
13579
10
7 9
10
C18 AS1
7 9
10
7 9
10
35735713579
10
1 3 5
AS2
135135357135579579579
AS3
13535779
10
13513579
10
7 9
10
AS4
357357135357579135579
C19 AS1
57913557979
10
579357579
AS2
57979
10
13579
10
357357135
AS3
7 9
10
13513557957913579
10
AS4
35735713557979
10
135135
C20 AS1
13557979
10
57979
10
35779
10
AS2
13513513557979
10
35779
10
AS3
7 9
10
135135579579579579
AS4
579357357357579357579
C21 AS1
35713513535735779
10
3 5 7
AS2
135135135579135357579
AS3
7 9
10
7 9
10
13513579
10
579579
AS4
13579
10
13535779
10
35779
10
C22 AS1
13557979
10
135579579135
AS2
57935779
10
57979
10
57979
10
AS3
13557935779
10
35779
10
7 9
10
AS4
579579135135579357135
Table A2. Integrated decision matrix.
AS1 AS2 AS3 AS4
C1 5.000 7.000 8.714 5.000 7.000 8.571 6.143 8.143 9.571 5.286 7.286 8.857
C2 4.143 6.143 7.714 4.429 6.429 8.143 2.714 4.714 6.714 3.286 5.286 7.286
C3 5.286 7.286 8.857 4.571 3.857 5.857 7.714 5.571 7.429 3.857 5.857 7.714
C4 3.857 5.857 7.714 3.000 5.000 6.857 3.000 5.000 6.857 3.571 5.571 7.286
C5 3.571 5.571 7.571 3.857 5.857 7.857 4.429 6.429 8.143 5.286 7.286 9.000
C6 3.286 5.286 7.143 4.143 6.143 7.857 5.286 7.286 8.857 5.000 7.000 8.714
C7 4.714 6.714 8.286 3.571 5.571 7.286 5.000 7.000 8.571 5.000 7.000 8.714
C8 4.143 6.143 7.857 5.571 7.571 9.143 3.571 5.571 7.429 2.714 4.714 6.714
C9 3.571 5.571 7.429 3.857 5.857 7.714 3.571 5.571 7.286 4.429 6.429 8.143
C10 5.000 7.000 8.714 4.143 6.143 7.714 3.571 5.571 7.286 4.143 6.143 7.857
C11 3.857 5.857 7.857 3.571 5.571 7.286 3.000 5.000 7.000 4.714 6.714 8.286
C12 4.429 6.429 8.286 3.857 5.857 7.571 4.143 6.143 7.857 3.286 5.286 7.143
C13 4.429 6.429 8.143 3.571 5.571 7.286 3.571 5.571 7.286 4.143 6.143 7.857
C14 4.714 6.714 8.286 5.286 7.286 8.714 5.000 7.000 8.571 4.143 6.143 7.857
C15 3.286 5.286 7.000 5.286 7.286 8.714 5.286 7.286 8.857 4.429 6.429 8.143
C16 2.714 4.714 6.571 3.000 5.000 7.000 4.429 6.429 8.143 4.429 6.429 8.000
C17 2.429 4.429 6.429 4.429 6.429 8.000 4.429 6.429 8.143 4.714 6.714 8.286
C18 4.143 6.143 7.714 3.000 5.000 7.000 3.857 5.857 7.429 3.000 5.000 7.000
C19 4.429 6.429 8.286 3.857 5.857 7.571 3.857 5.857 7.571 3.000 5.000 6.857
C20 5.000 7.000 8.571 3.571 5.571 7.286 4.143 6.143 8.000 3.857 5.857 7.857
C21 3.000 5.000 6.857 2.429 4.429 6.429 4.714 6.714 8.286 4.143 6.143 7.714
C22 3.571 5.571 7.429 5.571 7.571 9.143 4.714 6.714 8.286 3.000 5.000 7.000
Max 8.857 9.143 9.571 9.000
Sustainability 2018,10, 2389 17 of 20
Table A3. Normalized decision matrix.
AS1 AS2 AS3 AS4
C1 0.565 0.790 0.984 0.547 0.766 0.938 0.642 0.851 1.000 0.587 0.810 0.984
C2 0.468 0.694 0.871 0.484 0.703 0.891 0.284 0.493 0.701 0.365 0.587 0.810
C3 0.597 0.823 1.000 0.500 0.422 0.641 0.806 0.582 0.776 0.429 0.651 0.857
C4 0.435 0.661 0.871 0.328 0.547 0.750 0.313 0.522 0.716 0.397 0.619 0.810
C5 0.403 0.629 0.855 0.422 0.641 0.859 0.463 0.672 0.851 0.587 0.810 1.000
C6 0.371 0.597 0.806 0.453 0.672 0.859 0.552 0.761 0.925 0.556 0.778 0.968
C7 0.532 0.758 0.935 0.391 0.609 0.797 0.522 0.731 0.896 0.556 0.778 0.968
C8 0.468 0.694 0.887 0.609 0.828 1.000 0.373 0.582 0.776 0.302 0.524 0.746
C9 0.403 0.629 0.839 0.422 0.641 0.844 0.373 0.582 0.761 0.492 0.714 0.905
C10 0.565 0.790 0.984 0.453 0.672 0.844 0.373 0.582 0.761 0.460 0.683 0.873
C11 0.435 0.661 0.887 0.391 0.609 0.797 0.313 0.522 0.731 0.524 0.746 0.921
C12 0.500 0.726 0.935 0.422 0.641 0.828 0.433 0.642 0.821 0.365 0.587 0.794
C13 0.500 0.726 0.919 0.391 0.609 0.797 0.373 0.582 0.761 0.460 0.683 0.873
C14 0.532 0.758 0.935 0.578 0.797 0.953 0.522 0.731 0.896 0.460 0.683 0.873
C15 0.371 0.597 0.790 0.578 0.797 0.953 0.552 0.761 0.925 0.492 0.714 0.905
C16 0.306 0.532 0.742 0.328 0.547 0.766 0.463 0.672 0.851 0.492 0.714 0.889
C17 0.274 0.500 0.726 0.484 0.703 0.875 0.463 0.672 0.851 0.524 0.746 0.921
C18 0.468 0.694 0.871 0.328 0.547 0.766 0.403 0.612 0.776 0.333 0.556 0.778
C19 0.500 0.726 0.935 0.422 0.641 0.828 0.403 0.612 0.791 0.333 0.556 0.762
C20 0.565 0.790 0.968 0.391 0.609 0.797 0.433 0.642 0.836 0.429 0.651 0.873
C21 0.339 0.565 0.774 0.266 0.484 0.703 0.493 0.701 0.866 0.460 0.683 0.857
C22 0.403 0.629 0.839 0.609 0.828 1.000 0.493 0.701 0.866 0.333 0.556 0.778
Table A4. Weighted normalized decision matrix.
AS1 AS2 AS3 AS4
C1 4.182 4.465 4.823 4.051 4.326 4.596 4.754 4.806 4.902 4.350 4.574 4.824
C2 3.465 3.918 4.269 3.588 3.972 4.366 2.101 2.783 3.439 2.704 3.318 3.968
C3 4.421 4.647 4.902 3.704 2.383 3.140 5.970 3.289 3.805 3.175 3.677 4.202
C4 3.226 3.736 4.269 2.431 3.090 3.676 2.322 2.951 3.512 2.939 3.497 3.968
C5 2.987 3.554 4.190 3.125 3.619 4.213 3.427 3.795 4.170 4.350 4.574 4.902
C6 2.748 3.372 3.953 3.356 3.796 4.213 4.091 4.301 4.536 4.115 4.394 4.746
C7 3.943 4.283 4.586 2.894 3.443 3.906 3.870 4.132 4.390 4.115 4.394 4.746
C8 3.465 3.918 4.349 4.514 4.679 4.902 2.764 3.289 3.805 2.234 2.959 3.657
C9 2.987 3.554 4.111 3.125 3.619 4.136 2.764 3.289 3.731 3.645 4.036 4.435
C10 4.182 4.465 4.823 3.356 3.796 4.136 2.764 3.289 3.731 3.410 3.856 4.279
C11 3.226 3.736 4.349 2.894 3.443 3.906 2.322 2.951 3.585 3.880 4.215 4.513
C12 3.704 4.101 4.586 3.125 3.619 4.059 3.206 3.626 4.024 2.704 3.318 3.890
C13 3.704 4.101 4.507 2.894 3.443 3.906 2.764 3.289 3.731 3.410 3.856 4.279
C14 3.943 4.283 4.586 4.282 4.502 4.672 3.870 4.132 4.390 3.410 3.856 4.279
C15 2.748 3.372 3.874 4.282 4.502 4.672 4.091 4.301 4.536 3.645 4.036 4.435
C16 2.270 3.007 3.637 2.431 3.090 3.753 3.427 3.795 4.170 3.645 4.036 4.357
C17 2.031 2.825 3.558 3.588 3.972 4.289 3.427 3.795 4.170 3.880 4.215 4.513
C18 3.465 3.918 4.269 2.431 3.090 3.753 2.985 3.457 3.805 2.469 3.139 3.813
C19 3.704 4.101 4.586 3.125 3.619 4.059 2.985 3.457 3.878 2.469 3.139 3.735
C20 4.182 4.465 4.744 2.894 3.443 3.906 3.206 3.626 4.097 3.175 3.677 4.279
C21 2.509 3.189 3.795 1.968 2.737 3.447 3.648 3.963 4.243 3.410 3.856 4.202
C22 2.987 3.554 4.111 4.514 4.679 4.902 3.648 3.963 4.243 2.469 3.139 3.813
References
1.
Behzadi, G.; O’Sullivan, M.J.; Olsen, T.L.; Zhang, A. Agribusiness supply chain risk management: A review
of quantitative decision models. Omega 2017. [CrossRef]
2.
Filho, C.P.M.; Caleman, S.M.Q.; Cunha, C.F. Governance in agribusiness organizations: Challenges in the
management of rural family firms. Rev. Adm. 2017,52, 81–92. [CrossRef]
3. Rothaermel, F. Strategic Management: Concepts and Cases; McGraw-Hill: New York, NY, USA, 2013.
4.
Tseng, M.-L. Using linguistic preferences and grey relational analysis to evaluate the environmental
knowledge management capacity. Expert Syst. Appl. 2010,37, 70–81. [CrossRef]
Sustainability 2018,10, 2389 18 of 20
5.
Al-Hakim, L.A.Y.; Shahizan, H. Knowledge management strategies, innovation, and organisational
performance: An empirical study of the Iraqi MTS. J. Adv. Manag. Res. 2013,10, 58–71. [CrossRef]
6.
López-Nicolás, C.; Meroño-Cerdán, Á.L. Strategic knowledge management, innovation and performance.
Int. J. Inf. Manag. 2011,31, 502–509. [CrossRef]
7.
García Del Junco, J.; De Reyna Zaballa, R.; GarcíaÁlvarez de Perea, J. Evidence-based administration for
decision making in the framework of knowledge strategic management. Learn. Organ.
2010
,17, 343–363.
[CrossRef]
8.
Venkitachalam, K.; Willmott, H. Strategic knowledge management—Insights and pitfalls. Int. J. Inf. Manag.
2017,37, 313–316. [CrossRef]
9.
Martinsons, M.G.; Davison, R.M.; Huang, Q. Strategic knowledge management failures in small professional
service firms in China. Int. J. Inf. Manag. 2017,37, 327–338. [CrossRef]
10.
Venkitachalam, K.; Willmott, H. Factors shaping organizational dynamics in strategic knowledge
management. Knowl. Manag. Res. Pract. 2015,13, 344–359. [CrossRef]
11.
Garavelli, C.; Gorgoglione, M.; Scozzi, B. Knowledge management strategy and organization: A perspective
of analysis. Knowl. Process Manag. 2004,11, 273–282. [CrossRef]
12.
Lwoga, E.T.; Ngulube, P.; Stilwell, C. Managing indigenous knowledge for sustainable agricultural
development in developing countries: Knowledge management approaches in the social context.
Int. Inf. Libr. Rev. 2010,42, 174–185. [CrossRef]
13.
Tseng, M.L.; Lim, M.K.; Wong, W.P.; Chen, Y.C.; Zhan, Y. A framework for evaluating the performance of
sustainable service supply chain management under uncertainty. Int. J. Prod. Econ.
2018
,195, 359–372.
[CrossRef]
14.
Li, J.; Pan, S.Y.; Kim, H.; Linn, J.H.; Chiang, P.C. Building green supply chains in eco-industrial parks towards
a green economy: Barriers and strategies. J. Environ. Manag. 2015,162, 158–170. [CrossRef] [PubMed]
15.
Rudnik, K.; Kacprzak, D. Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in
a manufacturing system. Appl. Soft Comput. 2017,52, 1020–1041. [CrossRef]
16.
Sehnem, S.; Oliveira, G.P. Analysis of the supplier and agribusiness relationship. J. Clean. Prod.
2017
,168,
1335–1347. [CrossRef]
17.
Fabbe-Costes, N.; Roussat, C.; Taylor, M.; Taylor, A. Sustainable supply chains: A framework for
environmental scanning practices. Int. J. Oper. Prod. Manag. 2014,34, 664–694. [CrossRef]
18.
Hernaus, T.; Mikuli´c, J. Work characteristics and work performance of knowledge workers. EuroMed J. Bus.
2014,9, 268–292. [CrossRef]
19.
Abdel Aziz, H.H.; Rizkallah, A. Effect of organizational factors on employees’ generation of innovative
ideas: Empirical study on the Egyptian software development industry. EuroMed J. Bus.
2015
,10, 134–146.
[CrossRef]
20.
Bloodgood, J.M. Organizational routines as mechanisms for knowledge creation, utilization, and storage.
In Knowledge Management and Organizational Learning; King, W.R., Ed.; Springer US: Boston, MA, USA, 2009;
pp. 41–58.
21. Duhon, B. It’s all in our heads. Inform 1998,12, 8–13.
22.
Bueno, E.; Aragón, J.A.; Salmador, M.P.; García, V.J. Tangible slack versus intangible resources: The influence
of technology slack and tacit knowledge on the capability of organisational learning to generate innovation
and performance. Int. J. Technol. Manag. 2010,49, 314–337. [CrossRef]
23.
Lee, H.; Choi, B. Knowledge management enablers, processes, and organizational performance:
An integrative view and empirical examination. J. Manag. Inf. Syst. 2003,20, 179–228. [CrossRef]
24. Krogh, G.V. Care in knowledge creation. Calif. Manag. Rev. 1998,40, 133–153. [CrossRef]
25.
Hansen, M.T.; Nohria, N.; Tierney, T. What’s your strategy for managing knowledge. Harvard Bus. Rev.
1999
,
77, 106–116.
26.
Gloet, M.; Terziovski, M. Exploring the relationship between knowledge management practices and
innovation performance. J. Manuf. Technol. Manag. 2004,15, 402–409. [CrossRef]
27.
Darroch, J. Knowledge management, innovation and firm performance. J. Knowl. Manag.
2005
,9, 101–115.
[CrossRef]
28.
Massey, A.P.; Montoya-Weiss, M.M.; Tony, M.O.D. Knowledge management in pursuit of performance:
Insights from nortel networks. MIS Q. 2002,26, 269–289. [CrossRef]
Sustainability 2018,10, 2389 19 of 20
29.
Kirchherr, J.; Reike, D.; Hekkert, M. Conceptualizing the circular economy: An analysis of 114 definitions.
Resour. Conserv. Recycl. 2017,127, 221–232. [CrossRef]
30.
Shankar, R.; Bhattacharyya, S.; Choudhary, A. A decision model for a strategic closed-loop supply chain to
reclaim End-of-Life Vehicles. Int. J. Prod. Econ. 2018,195, 273–286. [CrossRef]
31.
Tsao, Y.-C.; Linh, V.-T.; Lu, J.-C. Closed-loop supply chain network designs considering RFID adoption.
Comput. Ind. Eng. 2017,113, 716–726. [CrossRef]
32.
Hosoda, T.; Disney, S.M. A unified theory of the dynamics of closed-loop supply chains. Eur. J. Oper. Res.
2017
.
[CrossRef]
33.
Östlin, J.; Sundin, E.; Björkman, M. Importance of closed-loop supply chain relationships for product
remanufacturing. Int. J. Prod. Econ. 2008,115, 336–348. [CrossRef]
34.
Akçalı, E.; Çetinkaya, S. Quantitative models for inventory and production planning in closed-loop supply
chains. Int. J. Prod. Res. 2011,49, 2373–2407. [CrossRef]
35.
Pagell, M.; Shevchenko, A. Why research in SSCM should have no future. J. Supply Chain Manag.
2014
,50,
44–55. [CrossRef]
36.
Marshall, D.; McCarthy, L.; Heavey, C.; McGrath, P. Environmental and social supply chain management
sustainability practices: Construct development and measurement. Prod. Plan. Control
2015
,26, 673–690.
[CrossRef]
37.
Börjeson, N.; Gilek, M.; Karlsson, M. Knowledge challenges for responsible supply chain management
of chemicals in textiles—As experienced by procuring organisations. J. Clean. Prod.
2015
,107, 130–136.
[CrossRef]
38.
Kumar, V.; Koehl, L.; Zeng, X. A fully yarn integrated tag for tracking the international textile supply chain.
J. Manuf. Syst. 2016,40, 76–86. [CrossRef]
39.
Tseng, M.-L. Using a hybrid MCDM model to evaluate firm environmental knowledge management in
uncertainty. Appl. Soft Comput. 2011,11, 1340–1352. [CrossRef]
40.
Aliewi, A.; El-Sayed, E.; Akbar, A.; Hadi, K.; Al-Rashed, M. Evaluation of desalination and other strategic
management options using multi-criteria decision analysis in Kuwait. Desalination
2017
,413, 40–51.
[CrossRef]
41.
Patil, S.K.; Kant, R. A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management
adoption in Supply Chain to overcome its barriers. Expert Syst. Appl. 2014,41, 679–693. [CrossRef]
42.
Li, M.; Jin, L.; Wang, J. A new MCDM method combining QFD with TOPSIS for knowledge management
system selection from the user’s perspective in intuitionistic fuzzy environment. Appl. Soft Comput.
2014
,21,
28–37. [CrossRef]
43.
Ng, A.H.H.; Yip, M.W.; Din, S.B.; Bakar, N.A. Integrated knowledge management strategy: A preliminary
literature review. Procedia Soc. Behav. Sci. 2012,57, 209–214. [CrossRef]
44.
Huang, F.; Gardner, S.; Moayer, S. Towards a framework for strategic knowledge management practice:
Integrating soft and hard systems for competitive advantage. VINE J. Inf. Knowl. Manag. Syst.
2016
,46,
492–507. [CrossRef]
45.
Kim, T.H.; Lee, J.-N.; Chun, J.U.; Benbasat, I. Understanding the effect of knowledge management strategies
on knowledge management performance: A contingency perspective. Inf. Manag.
2014
,51, 398–416.
[CrossRef]
46.
Zhang, P.; Ng, F.F. Explaining knowledge-sharing intention in construction teams in Hong Kong.
J. Constr. Eng. Manag. 2013,139, 280–293. [CrossRef]
47.
Ward, J.; Aurum, A. Knowledge management in software engineering-describing the process. In Proceedings
of the 2004 Australian Software Engineering Conference, Melbourne, Australia,
13–16 April 2004;
pp. 137–146.
48.
Pawlowski, J.M.; Bick, M. The global knowledge management framework: Towards a theory for knowledge
management in globally distributed settings. Electron. J. Knowl. Manag. 2012,10, 92–108.
49.
Hwang, C.L.; Yoon, K. Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey;
Springer: New York, NY, USA, 1981.
50.
Ho, K.L.P.; Nguyen, C.N.; Adhikari, R.; Miles, M.P.; Bonney, L. Leveraging innovation knowledge
management to create positional advantage in agricultural value chains. J. Innov. Knowl. 2017. [CrossRef]
51.
Lundvall, B.Å.; Nielsen, P. Knowledge management and innovation performance. Int. J. Manpow.
2007
,28,
207–223. [CrossRef]
Sustainability 2018,10, 2389 20 of 20
52.
Tuamsuk, K.; Phabu, T.; Vongprasert, C. Knowledge management model of community business: Thai OTOP
champions. J. Knowl. Manag. 2013,17, 363–378. [CrossRef]
53.
Cui, L.; Wu, K.J.; Tseng, M.L. Selecting a remanufacturing quality strategy based on consumer preferences.
J. Clean. Prod. 2017,161, 1308–1316. [CrossRef]
54.
Cui, L.; Zhang, M.; Wu, K.J.; Tseng, M.L. Constructing a hierarchical agribusiness framework in Chinese belt
and road initiatives under uncertainty. Sustainability 2018,10, 251. [CrossRef]
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