Marina Pires de Lima Simão
State
University of Maringá (UEM), Brazil
E-mail:
mplsimao@gmail.com
Danilo
Hisano Barbosa
State
University of Maringá (UEM), Brazil
E-mail:
dhbarbosa@uem.br
Juliana
Sayuri Kurumoto
State
University of Maringá (UEM), Brazil
E-mail:
jskbarbosa2@uem.br
Gislaine
Camila Lapasini Leal
State
University of Maringá (UEM), Brazil
E-mail:
gclleal@uem.br
Edwin
Vladimir Cardoza Galdamez
State
University of Maringá (UEM), Brazil
E-mail:
evcgaldamez@uem.br
Syntia
Lemos Cotrim
State
University of Maringá (UEM), Brazil
E-mail: slcotrim2@uem.br
Submission: 12/07/2017
Accept: 02/03/2018
ABSTRACT
Reverse
Logistics includes the planning, implementation and control of the reverse flow
of post-sales and post-consumption goods. The purpose of this article is to
identify emerging collaborative networks and scientific areas on Reverse
Logistics (RL) using temporal, geospatial and topical analyses. The study is based
on the bibliometric networks analysis, a technique used to measure scientific
development, production indexes and dissemination of knowledge. The main
results of the research stand out the relationship of knowledge areas and
scientific gaps, the identification of the main authors and the aspects related
to the social network of cooperation of the authors such as country citation
and network density.
Keywords: reverse logistics; social
network analysis; collaborative networks
1. INTRODUCTION
Reverse
Logistics is a segment of Logistics focused on the movement and management of
products and resources in the post-sale and post-consumption (CSCMP, 2015). The
application of the reverse logistics system has grown as consumers' demands on
the social, economic and environmental impacts of products and packaging
discarded by society increase. It is a process being applied in several
industrial areas such as the plastics industry, packaging and tires.
The
demand for innovative solutions for the industry motivates scientific and
technological production. It is a production of knowledge observed from the
evolution of publication indicators of scientific articles. These references provide
knowledge for the implementation, modeling, evaluation and maintenance of
Reverse Logistics Systems.
Reverse
Logistics has become increasingly important due to growing concern about
environmental issues, legislation, social responsibility and sustainable
competitiveness (AGRAWAL et al., 2015; RAVI; SHANKAR, 2005). Govindan et al.
(2012) point out that Reverse Logistics is adopted as a strategic tool to
generate competitive advantage, both in terms of economic benefits and
corporate social image. Da Silva et al. (2017) complement that RL concern is
not only environmental, but also of adding value to the product, or minimizing
the use of raw materials incorporating post-consumer waste, and ensuring the
proper destination of what can’t be reused.
The
frontier between direct and reverse logistics is not strictly defined, since
the concepts of raw material and final customer can be relativized in some
productive chains (ADLMAIER; SELLITTO, 2007). Moraes et al. (2015) claim that
the term reverse logistics has been employed with widely varying meanings.
To
consider the growth observed in the area of Reverse Logistics and to analyze
the trends about the practice there are several forms of analysis, among them
the Bibliometric Networks. Bibliometric analysis is a quantitative and
statistical technique used to measure production indexes and dissemination of
knowledge, as well as accompanying scientific development in several areas or
patterns of authorship, publication and use of research results, helping
researchers to identify patterns that can influence the decision-making process
of the research (COSTA et al., 2012; DAIM et al., 2006).
The
Social Network Analysis (SNA) is a distinct subarea of research that science
uses as a complement to studies in the field of bibliometry (SILVA, 2006). It
is applied to phenomena in which the importance lies in the relations between
actors that interact in these phenomena (FARINA, 2004).
Facing
the expressed problematic, it is observed that there are studies such as
authors Autry et al. (2001), Pokharel and Mutha (2009) and Govindan et al.
(2015), who carried out analysis of the Business Logistics using the systematic
review and bibliometric analysis, however, rarely based on the analysis of
bibliometric networks.
The
objective of this study is to identify emerging scientific and technological
areas on Reverse Logistics using bibliometric networks. In addition, temporal,
geospatial and topical analyses were carried out from the softwares Sci2Tool
and CiteSpace.
The
Analysis of Social Networks of the authors through the indicators of scientific
production allows to determine the relations with technological and social
development and to identify the relationship of the knowledge areas of Reverse
Logistics & Engineering, Operations Research & Management Science,
Business & Economics and Management. With the research it is also possible
to draw gaps or new scientific opportunities that are emerging between Reverse
Logistics and Environmental Sciences, Green Sustainable Science Technology and
Computer Science Artificial Intelligence.
The
paper is structured in four sections, besides this introductory section.
Section 2 describes the methodological procedures adopted. In Section 3 the
theoretical reference is described, addressing reverse logistics and
bibliometric analysis. Section 4 presents the results obtained. Finally, in
Section 5 the conclusion is presented, highlighting the contributions and
limitations of this study.
2. METHODOLOGICAL PROCEDURES
Figure
1 illustrates the methodological procedures adopted, highlighting the steps
(Data Collection, Processing and Analysis) followed, activities involved and
their chaining.
Figure 1: Methodological procedures adopted.
The
Data Collection stage aims to
prepare the data for processing by software of bibliometric analysis and
involves the following activities
· Define the theme: this activity
involves the definition of the topic to be approached. It is important to
emphasize that this definition must have terminological precision in the
scientific field.
· Define filters: this activity
directs the search of the publications, through the definition of databases and
search filters to be used. The filters used were: Research Areas (Science
Technology and Others Topics, Engineering, Transportation, Business Economics
and Operations Research Management Science); Types of Document (Articles); And
finally, publications that are only part of the Web of Science database.
· Extract database: This activity
is the initial step to open the network analysis and aims to create the
database. Data was extracted in April 2016 from the Web of Science platform and
the formulated database contained 841 documents.
The Processing stage aims to prepare the
database and the formulation of the graphs and involves the following
activities:
· Select the software: this activity
allows the study of the software available for the elaboration of network
graphs, identifying which types of networks can be generated and how to produce
them. For this study, the software CiteSpace and Sci2Tool were selected.
· Prepare database: this activity
aims to prepare the Database for compatibility with the software used.
The objective of
the Analysis stage is to perform the
analytical treatment of the graphs in a fragmented and systemic way, relating
it to the unit of analysis of the research, Reverse Logistics and its aspects. This stage involved the
following activities:
· Draw network graphs: this activity
aims to determine the types of networks that are pertinent to the study and
generate the network graphs. In this activity, CiteSpace was used to
graphically identify the evolution of the literature, and still detect and
visualize the emergence of trend and radical changes in the literature in a
given timing period, following the assumptions Chen et al. (2006). The
following graphs were obtained from CiteSpace: Co-Authoring Network,
Co-Occurrence Network of Research Areas and Co-Authoring Networks by country.
In addition, Sci2Tool software was used to obtain the Network of Quotations and
Network of Co-Quotations.
· Analyse the networks: this activity
involves the study of the interpretation types that can be generated in each of
the networks. In addition, this activity aims to find connections between the
networks that have been produced, how they can interconnect with each other and
with the study, the common factors that are relevant, and what interpretations
can be done, and so on. The Sci2Tool software was used for the analysis and
visualization of bibliometric networks under the temporal, geospatial and
topical perspectives, as highlighted by SciTeam (2009).
3. REVERSE LOGISTICS
Reverse
Logistics is the process of planning, implementing and controlling the
efficient and effective flow of obsolete materials such as raw material,
process inventory, finished products and related information, from the point of
consumption to the point of origin, for the purpose of recapturing value or
proper disposal (ROGERS; TIBBEN-LEMBKE, 1998; ROGERS; TIBBEN-LEMBKE, 2001;
SHERIFF; GUNASEKARAN; NACHIPPAN, 2012).
Reverse
Logistics is subdivided into two components: post-consumption and post-sale
(FLEISHMANN, 2000). Reverse Logistics post-sale has as strategic objective to
add value to a logistics product that is returned for commercial reasons,
errors in order processing, manufacturer's warranty, defects or malfunctions in
the product, transportation breakdown, among other reasons , and consists of
end-of-use return, commercial return, guarantee return, scrap production and
byproducts, and packaging (DU; EVANS, 2008).
The
end-of-use return are those goods discarded after their use is completed,
commercial return is referred to the return of products undoing a previous
trade, warranty return are the goods that have failed during use, or damaged
during delivery and return to the original sender. Scrap production and
byproducts refers to the excess materials in the production process, and
finally the packaging, which can be as example reusable bottles and pallets
(FLEISHMANN, 2000).
Post-consumer
Reverse Logistics aims to add value to a logistic product made up of goods that
were useless to the original owner, or that still have conditions of use, for
products that have been discarded because they have reached the end of their
useful lives and for industrial waste. This includes five recovery options:
repair, remodeling, remanufacturing, cannibalization and recycling (THIERRY et
al., 1995).
4. BIBLIOMETRIC ANALYSIS
According to Pritchard (1969), the term bibliometric
is used for the application of mathematical and statistical methods for books
and other means of communication. In addition, the creation of significant and
rigorous indicators is a complex activity. However, it is important to
emphasize the growing importance that indicators of scientific production are
gaining as instruments for the analysis of scientific activity and its
relations with economic and social development (KOBASHI; SANTOS, 2006). The
authors also point out the occurrence of expressive set of bibliometric
indicators used in the analysis of scientific production, as follow:
· Scientific production indicators: constructed
by counting the number of publications by document type, by institution, area
of knowledge, country, etc.
· Citation Indicators: constructed by counting
the number of citations received by an article published in a journal (most
recognized way of assigning credit to the author).
· Connection indicator: built by the
co-occurrence of authorship, citations and words, being applied in the
elaboration maps of knowledge structures and networks of relationships between
researchers, institutions and countries.
· Indicators of scientific quality: constructed
from the perception and opinion of peers who evaluate publications according to
their content.
· Indicators of scientific activity: based on
the accounting of scientific activities developed, namely the number and
distribution of published papers, author productivity, collaboration in
authorship, number and distribution of references between studies and authors,
among others.
· Scientific Impact Indicators: constructed from
two subgroups, research impact indicators where the number of citations
received and indicators of the sources impact are indicated; where that is the
impact factor of journals, index of immediate citation and influence of
journals.
· Indicators of thematic associations: built
from analysis of citations and references.
In addition to these indicators, bibliometric analysis
occurs from various perspectives, including interpretations from temporal,
geospatial, and topical analysis. The Temporal Analysis intends to identify the
nature of the phenomena represented by a sequence of observations, such as
patterns, trends, seasonality, outliers and explosions of activity. Geospatial
Analysis answers questions such as where something happens and what impact it
has in neighborhood areas. Topical Analysis extracts a set of unique words or
word profiles and their frequency from the body of a text; Network Analysis,
the main focus of the study, is based on the analysis of social networks,
physics, information science, bibliometric, scientometry, econometrics,
infometrics, webometric, communication theory, sociology of science and several
other disciplines (WEINGART et al., 2010).
5. RESULTS
In the sequence are the results on the analysis of
co-occurrence networks of categories, co-authorship and citations. The main
objectives are to identify the relationships and gaps of the categories or
areas of knowledge developed, to identify the social network of authors from
the scientific production and to identify relevant factors of the exchanges or
collaborations carried out by the scientific community in the area of Reverse
Logistics, among others.
5.1.
Analysis
of the category co-occurrence network
Co-occurrence Network Analysis uses co-occurrence
patterns of pairs of items, i.e., words or phrases to identify the relationship
between ideas within an area present in the text (HE, 1999). This analysis
allows a new researcher in an area to become instantly familiar, facilitating
the identification of key themes and their relationships, in addition to
discovering and describing the interaction between different research fields
(MUÑOZ-LEIVA
et al., 2012). The graphical representation on the Analysis of the
Categories Co-occurrence Network is highlighted in Figure 2.
Figure
2: Categories Co-Occurrence Network
The areas of research with more occurrence were
Engineering, Operations Research & Management Science, Business &
Economics and Management. It is noted that research fronts are correlated,
since Engineering strives to achieve results by designing economically and
technically feasible solutions. Operations Research & Management Science
enables more effective decision making, and builds more efficient systems,
Business & Economics deals with the organization, management, expansion and
strategy, and finally Management promotes the planning, control and improvement
of the processes involved in Reverse Logistics.
The incidence of Categories (Engineering, Operations
Research & Management Science, Business & Economics and Management)
demonstrates a complementarity of methods and practices developed in the areas
for the Reverse Logistics process. In addition, the most cited articles and
authors that have the greatest influence in the area are allocated in these
categories.
The network analysis also indicates a density degree
of 0.1262, which implies a low degree of direct interconnection between the
agents and characterizes the network as diffuse. As pointed out by Gnyawali and
Madhavan (2001) and Sacomano Neto (2004), diffuse networks provide innovation
due to the non-redundant character between nodes.
It is also possible to observe that other focuses of
study have also arisen since the Reverse Logistics can be applied in diverse
scopes. In the future and with the feasibility of correlating Reverse Logistics
with different areas of knowledge, it is possible that the network
characteristic of being diffuse changes and becomes a dense network. From an
analysis performed on the Web of Science platform it is observed that the areas
Environmental Sciences, Green Sustainable Science Technology and Computer
Science Artificial Intelligence are emerging in the theme and have different
coverage of the pioneers on the subject.
From the Environmental Sciences category, it is
observed several studies related to sustainability, green supply chain
management, hybrid systems, waste management and other concepts, but always
focused on environmental conservation. In addition, it is noted that some
approaches deal with post-consumption and post-sales practices such as
remanufacturing, remodeling, end-of-use return, recycling, among others, which
demonstrates that Thierry (1995) and Fleishmann (2000) already presented
concepts that would be of great relevance for the development of studies.
In the Green Sustainable Science Technology area,
study approaches are similar to the above category, and many articles fitting
within this category are noted to similarly fit under Environmental Sciences.
Such a characteristic can be due to the affinity of both fields.
Another front of study that has been emerging from the
technological advances of the area of Computation and that increasingly seeks
automation is that of Computer Science Artificial Intelligence. In this area,
the authors focus on algorithm studies for problem solving, programming model
development, program management, among others, which are usually focused on
computation. One aspect that can be examined is the tendency of several works
to mention points such as green logistics, sustainable waste management, green
transport and development of green practices, which emphasizes the growth that
environmental practices have been happening, as well as concern with
post-consumption and post-sales.
5.2.
Analysis
of the co-authorship network
Figure 3 highlights the Co-Authorship Network.
According to Zare-Farashbandi et al. (2014), authors through their
participation in one or more publications indirectly demonstrate links between
them in this social network.
It is noted that most of the actors in the network do
not present any type of connection, reflecting the lack of scientific collaboration
on the subject. According to Lima (2009), there are innumerable causes directly
or indirectly related to the structural evolution of co-authorship networks, so
it is only necessary to restrict the analysis to specific elements such as
lines, projects and research groups to which the network actors are linked.
Following this line of reasoning, what may be the possible causes of this low
adherence to the association of studies? Some factors can be punctuated as weak
incentive to share ideas, geospatial factors and reasons for topical
divergence.
It is also indicated in Figure 3 the most significant
loops of the network (circle). The following social network of authors forms
the group that shows the highest density: Kannan Govindan (University of Southern
Denmark), Devika Kannan (Aalborg University), Ali Diabat (North Carolina State
University) and Joseph Sarkis (Worcester Polytechnic Institute) - highlighted
in Figure 4. The network in which they are inserted is of strong connection and
dense, and has as central actor Kannan Covindan, because it has non-directional
relation (knowledge, co-authorship), that is, reciprocal relationship with each
one of the authors cited.
|
|
Figure 3:
Co-Authoring Network |
Figure 4: Nucleus of Higher Density of the Co-Authorship Network |
Analyzing other aspects of the network's most
significant relationships, it is recognized that the link between Devika Kannan
and Kannan Govindan (21 co-authors) may have a geospatial character, which
favors the bonding between them and facilitates the conservation of the link.
The character of a network is not restricted only to geospatial factors and can
be seen in the central actor's connection with Ali Diabat (9 co-authors) and
Joseph Sarkis (10 co-authors). The relational link between them may be topical,
explained by the interest in similar approaches to the topic or a joint
research group.
5.3.
Analysis
of the country co-authoring network
Figure 5 illustrates the Analysis of the Co-authoring
Network by Country, it is noticed that it is a network with dense
interconnection presenting as central actor the United States. Other relevant
countries in the network structure are Canada, China and Iran. Evidence that
may result in the prominence of these countries is the number of
Technology-Based Companies (TBC's) that generate new business models and
develop innovations, requiring interdisciplinary of Knowledge areas.
Figure
5: Network of Co-authorship by Country
There are numerous definitions for these companies. However,
by uniting concepts from Balkin et al. (2000), Grinstein and Goldman (2006),
Löfsten and Lindelöf (2005) and Machado et al. (2001), the conclusion is that
they are organizations based on activities of development and production of
innovations, based on the systematic application of scientific and
technological knowledge and use of advanced and pioneering techniques. Its main
inputs are the knowledge and technical-scientific information, present high
expenses with Research & Development, besides employing large amount of
technical-scientific and engineering personnel.
The development of TBC's is due to the movement of
Science Parks. The term Science Parks is designed to describe a private
initiative with formal and operational links with universities, higher
education institutions and research centers. It is designed for research and
business development based on the sharing of knowledge of other organizations
that participate in the technological pole, and which have an administrative
function engaged with the transfer of technology and entrepreneurial skills to
the companies located there (MACHADO et al., 2001).
Data available by Unesco (2016) indicate the number of
parks in the most relevant countries in the co-author network: China 80, USA
72, Canada 13 and Iran 3. Organizational environment that naturally encourages
national economy promotes innovation and demands the development of scientific
knowledge of these countries.
5.4.
Analysis
of the author citations network
According to Foresti (1989), a description for
citation analysis would be a part of Bibliometry responsible for investigating
the relationships between citing documents and cited documents considering as
units of analysis, integrally or in their various parts: author, title,
geographical origin, year and Language of publication, etc. With the help of
the Sci2Tool software, an Author Citation Network was created, which can be
observed in Figure 6.
Figure
6: Citation Network
Approaching the Network of Quotations in its focus of
greater density Figure 7 is obtained. In this, it is identified that the
represented authors are analogous to those that are in the Report of
Quotations. This report was extracted from the Web of Science online platform
and is represented in Table 1.
Figure
7: Citation Network Higher Density Focus
Table 1: Citations Report
|
Title |
Authors |
Citations |
Publication Year |
1 |
Quantitative models for reverse
logistics: A review |
Fleischmann, M; BloemhofRuwaard, JM;
Dekker, R; VanderLaan, E; VanNunen, JAEE; VanWassenhove, LN. |
727 |
1997 |
2 |
Closed-loop supply chain models with
product remanufacturing |
Savaskan, RC; Bhattacharya, S; Van
Wassenhove, LN. |
561 |
2004 |
3 |
Green supply-chain management: A
satate-of-the-art literature review |
Srivastava, Samir K. |
560 |
2007 |
4 |
Facility location and supply chain
management - A review |
Melo, M. T; Nickel, S; Saldanha-da-Gama,
F. |
368 |
2009 |
5 |
A characterisation of logistics network
for product recovery |
Fleischmann, M; Krikke, HR; Dekker, R;
Flapper, SDP. |
313 |
2000 |
6 |
Sustainable supply chain: An introduction |
Linton, Johnathan D; Klassen, Robert;
Jayaraman, Vaidyanathan. |
293 |
2007 |
7 |
A closed-loop logistics model for
remanufacturing |
Jayaraman, V; Guide, VDR; Srivastava, R. |
268 |
1999 |
8 |
The impact of product recovery on
logistics network design |
Fleischmann, M; Beullens, P;
Bloemhof-Ruwaard, JM; Van Wassenhove, LN. |
253 |
2001 |
These results indicate the importance of the authors
in the academic field of Reverse Logistics, since they represent the main
sources of data chosen by the scientific community. One can interpret such
prominence of these authors through the idea of Evolution of Knowledge. The
production of knowledge is part of an aspect of human existence and according
to Souza and Morais (2012) one experiences an era characterized by the
technical-scientific revolution that greatly facilitated access to information.
However, even if a wide variety of study material is
available, each new topic stems from a problem that has repercussions on
reflection, search for explanations and solutions. Following this line of
thought, it can be highlighted that the problematic Reverse Logistics had its
first reflections and solutions from the authors like Fleishmann M., Savaska
R., because their studies deal with a base reference for the development of
research on Reverse Logistics.
Another co-cited network is co-citation and is
represented in Figure 8. The co-citation analysis studies are based on the
co-occurrence of two authors or documents of scientific production and
highlight the knowledge structure of a given front, according to the perception
of the citing community.
Figure
8: Co-Citation Network
The co-citation analysis refers to the co-occurrence
of two authors or publications, based on the premise that when two authors or
publications are cited together in a later publication there are indications of
subject proximity between those cited from the perspective of citing. Thus, the
number of times that two articles, authors, and journals are cited together in
a third article is counted (MIGUEL et al., 2008).
From the network, it is observed that M. Fleischmann
is one of the main authors of citations and in the network of citations, he is
one of the largest nodes in the Network of Citations, once again showing its
significant influence in the academic field when it comes to the topic Reverse
Logistics.
6. FINAL CONSIDERATIONS
The paper contributes to a bibliometric analysis of
the subject Reverse Logistics using bibliometric networks and applying Social
Network Analysis. It is verified when, looking for other literary analysis,
that this type of study using bibliometric networks is novel, being rare the
studies that explore the same segment of ideas, even more when it comes to the
subject in question.
Another contribution of the research is that this
analysis allowed identifying the main areas where the reverse logistics is
approached, as well as, the interface of the subject with other areas; it
allowed to visualize groups of researches, the existing collaboration between
different authors and countries, the most cited papers and those that have
proximity of subjects. Given the analysis presented, the research also
contributes to the people who are starting the research on the subject, as it
assists in the visualization of the state of the art. As a secondary contribution
to this study, it is highlighted the systematization of the methodological
procedure that integrates bibliometric analysis and analysis of social
networks.
The use of bibliometric networks and the analysis of
social networks are important, since they allow to identify patterns of
cooperation and productivity of researchers, besides helping to better
understand themes, concepts and relationships between various subjects.
Thus, from the results on citations it was possible to
identify the author responsible for presenting theoretical contributions to the
field of knowledge of reverse logistics. This author is Fleishmann. Its
importance is shown to contain three related studies among the ten most cited.
The results of the analytical treatment corroborate with the descriptive report
of the number of citations, evidencing the importance of the author.
With regard to co-authorship networks, there is
collaboration between authors from different universities and countries. This
shows that there are no groups of institutions or countries segmenting
knowledge.
The co-citation network has again identified that the
author Fleishmann is the main node of the network. This means that it is the
author that most connects different groups of articles, that is, it represents
the knowledge base for different areas of research.
The research has some limitations; among them is the
fact that some analysis have not been explored, for example the modeling where
the data models are grouped in descriptive and of processes (WEINGART et al., 2010).
Therefore, it is recommended that future researchers address these variables.
In addition, as a suggestion, the same methodology of the study is highlighted,
but on other research fronts such as the Green Logistics.
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