1- Supply chain finance A systematic literature review and bibliometric analysis

International Journal of Production Economics 204 (2018) 160–173
Contents lists available at ScienceDirect
International Journal of Production Economics
journal homepage: www.elsevier.com/locate/ijpe
Review
Supply chain finance: A systematic literature review and bibliometric
analysis
T
Xinhan Xua, Xiangfeng Chena, Fu Jiab,∗, Steve Brownc, Yu Gongc, Yifan Xua
a
School of Management, Fudan University, 670 Guoshun Road, Shanghai 200433, China
The York Management School, University of York, Heslington, York YO10 5DD, UK
c
Southampton Business School, University of Southampton, Southampton SO17 1BJ, UK
b
A R T I C LE I N FO
A B S T R A C T
Keywords:
Supply chain finance
Literature review
Bibliometric analysis
Network analysis
Co-citation
Content analysis
Supply Chain Finance (SCF) is an effective method to lower financing costs and improve financing efficiency and
effectiveness, and it has gained research momentum in recent years. This paper adopts a systematic literature
review methodology combined with bibliometric, network and content analysis based on 348 papers identified
from mainstream academic databases. This review provides insights not previously fully captured or evaluated
by other reviews on this topic, including key authors, key journals and the prestige of the reviewed papers. Using
rigorous bibliometric and visualisation tools, we identified four research clusters, including deteriorating inventory models under trade credit policy based on the EOQ/EPQ model; inventory decisions with trade credit
policy under more complex situations; interaction between replenishment decisions and delay payment strategies in the supply chain and roles of financing service in the supply chain. Based on the clusters identified, we
carried out a further content analysis of 112 papers, identifying research gaps and proposing seven actionable
directions for future research. The findings provide a robust roadmap for further investigation in this field.
1. Introduction
controlling the flow of financial resources on an inter-organisational
level.
Pfohl and Gomm (2009) define SCF as the inter-company optimisation of financing and the integration of financing processes with
customers, suppliers, and service providers to increase the value of all
participating companies. Gomm (2010) states that the objective of SCF
is to optimise financing across company borders to decrease the cost of
capital and accelerate cash flow. Pfohl and Gomm (2009) definition is
adopted in this paper. However, we realise that early studies in SCF
tend to focus on the effects of only one supply chain player's financing
on inventory, which has implications for other supply chain members
(supplier or retailer). To trace the complete evolution of the topic, we
include these early studies in this review. The methods we used to select
the papers are reflected in the search strings and keywords in the
methodology section.
SCF aims to optimise financial flows through solutions implemented
by financial institutions (Camerinelli, 2009) or technology providers
(Lamoureux and Evans, 2011). The ultimate objective is to align financial flows with product and information flows within the supply
chain, improving cash flow management from a supply chain perspective (Wuttke et al., 2013). The benefits of the SCF approach rely on
Supply Chain Finance (SCF) has played an increasingly important
role in operational and financial practices and attracted growing attention from academia and industry alike (Yan et al., 2016; Milder,
2008). The history of SCF research can be traced back to the 1970s, well
before the popularisation of the term supply chain management. For
example, Budin and Eapen (1970) study the net flow of cash generated
over the course of business operations in a cash planning period and
how such net inflows are affected by changes in policies concerning
trade credit and inventories. Haley and Higgins (1973) investigate the
relationship between inventory policy and trade credit policy in the
context of the basic lot-size model.
The first formal definition of SCF did not appear until the 2000s.
Stemmler (2002) finds that the key characteristic of SCF is the integration of financial flows into the physical supply chain, and SCF can
be characterised as an essential part of supply chain management.
Hofmann (2005) describes SCF as located at the intersection of logistics,
supply chain management, and finance and defines it as an approach
for two or more organisations in a supply chain, including external
service providers, to jointly create value by planning, steering, and
∗
Corresponding author.
E-mail addresses: xuxh15@fudan.edu.cn (X. Xu), chenxf@fudan.edu.cn (X. Chen), fu.jia@york.ac.uk (F. Jia), steve.brown@soton.ac.uk (S. Brown),
y.gong@soton.ac.uk (Y. Gong), yfxu@fudan.edu.cn (Y. Xu).
https://doi.org/10.1016/j.ijpe.2018.08.003
Received 9 September 2016; Received in revised form 31 July 2018; Accepted 2 August 2018
Available online 11 August 2018
0925-5273/ © 2018 Elsevier B.V. All rights reserved.
International Journal of Production Economics 204 (2018) 160–173
X. Xu et al.
Network analysis through bibliometric tools proves powerful for identifying established and emerging topical areas. These algorithmically
identified clusters set the stage for topical classification of the published
models and further investigation of the evolution of these clusters over
years. Based on the bibliometric and network analysis, we selected
papers published in 3, 4 and 4* journals of the Association of Business
Schools' Academic Journal Guide (AJG) from each cluster and carried
out a content analysis. From these results, we gained additional insights
into the current research themes and potential directions for future
research.
The AJG, which was called the ABS Guide before 2015, is recognised as an influential journal ranking system. It is based upon peer
review, editorial and expert judgements and is informed by statistical
information relating to citations. The guide not only is based on a
weighted average of journal metrics but also reflects the perceptions of
the subject experts and scholarly associations (AJG, 2015). We choose
the AJG because it has been widely adopted as a policy tool for staffing
in business schools (Kelly et al., 2009; Hussain, 2011) and is commonly
used by researchers in papers (Mingers and Willmott, 2013; Tüselmann
et al., 2016).
We make contributions to SCF research in the following ways. First,
this is one of the most comprehensive reviews of this emerging albeit
important topic in supply chain management. Second, the combination
of bibliometric and content analysis presents a methodological contribution to a systematic literature method. Third, the identification of
the four clusters and additional insights from the content analysis
provide a systematic knowledge structure for SCF based on which actionable ideas for future research are proposed.
The remainder of the paper begins with an introduction to the literature review methodology used and descriptive analysis in section 2.
Section 3 presents the results of bibliometric analysis and network
analysis (including citation and co-citation analysis). In Section 4, we
carry out a content analysis. Section 5 discusses the findings of all the
analysis and proposes numerous future research directions. Section 6
summarises the results and presents the limitations of this study.
cooperation among stakeholders within the supply chain and typically
result in lower debt costs, new opportunities to obtain loans (especially
for weak supply chain players) or reduced working capital within the
supply chain (Gelsomino et al., 2016).
Specifically, when we selected search strings and keywords, we
chose “trade credit” and “factoring” because trade credit, factoring and
reverse factoring are all specific solutions in SCF that should be included in the review of SCF. Trade credit is a short-term loan between
firms that is tied in both timing and value to the exchange of goods
between them (Ferris, 1981). Seifert et al. (2013) point out that the role
of trade credit in the global economy is extensive and consequently, it
has received considerable attention in the SCF research. Factoring is a
type of supplier financing in which firms independently sell their
creditworthy accounts receivable at a discount to a financier, the factor,
and receive immediate cash (Klapper, 2006; Soufani, 2002). Reverse
factoring is a financial arrangement in which a corporation facilitates
early payment of its trade credit obligations to suppliers; this arrangement has received considerable attention from both the business and
research communities (van der Vliet et al., 2015; Tanrisever et al.,
2012). In reverse factoring, the lender purchases accounts receivables
only from specific informationally transparent, high-quality buyers, and
the credit risk becomes the default risk of the high-quality buyers instead of the risky Small and Medium-sized Enterprise (SME), which
make it possible to provide low-risk financing to high-risk suppliers
(Klapper, 2006).
There have been a few literature reviews on certain themes of SCF.
These review papers cover one or two themes of SCF. For example,
Chang et al. (2008) provide a review of inventory lot-size models under
conditions of extended payment privileges. Birge (2015) discusses the
interaction of operations and finance in which financial activity has
significant potential to make an impact on operations and operational
considerations provide new perspectives for financial decisions. Zhao
and Huchzermeier (2015) propose a risk management framework for
the multidimensional integration of operations-finance interface
models.
However, there is no comprehensive review paper except for
Gelsomino et al. (2016). There are several significant differences between our review and that of Gelsomino et al. (2016): 1) Gelsomino's
study is focused on SCF concepts and solutions, while we review papers
containing all aspects of SCF; 2) We include a more comprehensive list
of keywords than Gelsomino's study and separate keywords into two
search strings (financing and supply related), which theirs does not; 3)
Gelsomino et al. (2016) identify more than 2000 articles and select 119
papers for final review, while our study examines more than 4700 articles and focuses on 348 papers for bibliometric analysis together with
112 papers for content analysis; 4) Gelsomino's study first covers papers
from a period between 2000 and 2014 and then examines the reference
of the papers selected in the first step based on a handful of elite
journals, while we trace back to the origin and did not put a time limit
or constrain the review with a set of journals for the bibliometric
analysis (we do constrain the journals as AJG 3, 4 and 4* for the content
analysis to be explained later); 5) Gelsomino's paper adopts only the
content analysis method for reviewing selected papers while we adopt a
combination of bibliometric and content analysis and the results are
significantly different. In a sense, we provide a more comprehensive
view of how SCF research developed and identify four clusters based on
rigorous mathematics tools, which are different from their themes and
advanced a different set of future research directions for SCF researchers. To illustrate the similarities and difference between Gelsomino's and our paper more explicitly, Table 1 is presented for comparison.
In this study, we first carried out a comprehensive and systematic
literature review and rigorous bibliometric and network analysis on
SCF (e.g., citation and co-citation analysis) to map out the knowledge
structure of this topic and then conducted a content analysis of the key
papers based on journal quality and the papers' PageRank score.
2. Research methodology and descriptive data analysis
Literature reviews aim to map and evaluate the body of literature to
identify potential research gaps and highlight the boundaries of
knowledge (Tranfield et al., 2003). Structured literature reviews are
typically completed through an iterative cycle of defining appropriate
search keywords, searching the literature, and completing the analysis
(Saunders et al., 2009). Rowley and Slack (2004) recommend a structured methodology for scanning resources, designing the mind map to
structure the literature review, writing the study and building the
bibliography. In this study, we follow a systematic literature review
methodology (Tranfield et al., 2003) combined with bibliometric and
network analysis for a comprehensive evaluation of the field, aiming to
identify the most influential studies and authors and the existing topical
areas of research and provide insights for current research interests.
2.1. Defining the appropriate search terms
The term SCF consists of two elements (i.e., supply chain and finance) and is cross-disciplinary, straddling supply chain management
and finance. To ensure that both aspects are fully captured by the
keywords, we included two search strings, which are shown in Table 2.
The first string is supply chain-related terms, including keywords such
as “supply chain”, “value chain”, “inventory” and “purchasing”
(Seuring and Müller, 2008). The second search string contains financerelated keywords such as “finance”, “financing”, “trade credit” and
“bank credit”. The keywords were chosen based on previous literature
reviews on similar topics, the authors' own research experience and
expert views from fellow SCF academics.
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International Journal of Production Economics 204 (2018) 160–173
X. Xu et al.
Table 1
Comparisons of Gelsomino et al. (2016) the current review.
Gelsomino et al. (2016)
Our paper
Time limit set
Keywords
A specific time frame (2000–2014)
A selected list of keywords and strings
Journals
Major logistics and supply chain management journals and
top finance and management journals
SCF concepts and solutions
Content analysis
Definitions of SCF reflect the “finance oriented”
perspective and the “supply chain oriented” perspective
No time limit (1970–2016)
A comprehensive list of supply chain related and finance
related keywords
AJG 3, 4, and 4* for content analysis;
No constrain for bibliometric analysis
All aspects of SCF
Bibliometric analysis, network analysis and content analysis
The two perspectives of SCF are reflected in keywords
selection.
Four clusters with different research focus are identified.
Seven directions:
Focus
Review methods
Findings
Gaps and future directions (Numbers refer to the
sequences of contents in both papers)
Four gaps/directions:
(1) No general theory of SCF
(2) Weak empirical-based holistic analyses on the
application of SCF
(3) Few assessment models consider the impact of SCF
programs on supply chain financial performance.
(4) Lack of tools for choosing SCF solutions for different
supply chains and objectives
finance; or 2) they mainly focused on finance but superficially touched
on the supply chain. To do this, two of the co-authors drew a table with
all 420 papers and a column to judge ‘include’ or ‘exclude’ or ‘unsure’
based on their independent reasons. We then compared the results and
reached agreements on all the items with which we did not agree initially.
Table 2
The search string and keywords.
Supply chain-related keywords: (supply chain) OR (value chain) OR logistics OR
inventory OR procurement OR purchasing OR sourcing
AND
Finance-related keywords: finance OR financing OR (trade credit) OR (bank credit)
OR (early payment) OR (dela* payment) OR (advanc* payment) OR (capital
constraint) OR (financial constraint) OR factoring OR (limited liability) OR (cash
conversion cycle)
2.3. Descriptive analysis
The 348 journal articles were published between 1970 and 2016.
Fig. 1 shows an upward trend in terms of the number of articles published per year since 2000. Before 2003, 1 or 2 papers were published
per year. This shows that the 348 papers are scattered approximately
120 journals, 56 of which have contributed 80.17% of all publications
reviewed. The top 12 journals have published 161 of these identified
articles, representing approximately 46.26% of the 348 papers. Table 4
shows the top 12 journals that published the largest number of these
papers. It can be seen that the majority of the journals are operations
research journals.
2.2. Search results
We followed a three-step approach to identify the papers for final
review. First, in the Scopus and EBSCO databases, we collected and
stored journal articles (conference papers, books and chapters of books
excluded) for the defined search terms with an open starting time to
include as many publications as possible up to December 2016. The
initial search attempts identified 4787 titles (2381 papers in EBSCO and
2406 papers in Scopus). The search results were stored in CSV format to
include all the essential paper information such as paper title, authors'
names and affiliations, abstract, keywords and references.
There is some degree of overlap between the two databases. After
eliminating duplicates, 4078 papers remained. Second, we reviewed the
titles and abstracts, applying inclusion and exclusion criteria (Table 3).
In particular, studies focusing on the effects of one supply chain player's
financing on their inventory, which has implications for other supply
chain members (suppliers or retailers), are included. Four hundred and
twenty journal articles were selected for the third round of selection.
Third, we reviewed the full text of these papers applying the same inclusion and exclusion criteria and identified 348 journal articles for
bibliometric analysis. Papers were excluded when 1) they mainly focused on supply chain management but superficially touched on
3. Bibliometric and network analysis
3.1. Description of bibliometric and network analysis
3.1.1. Bibliometric analysis
Bibliometric analysis offers additional data statistics including author, affiliation and keywords. Several software packages such as
HistCite and BibExcel have been used in the past for bibliometric
analysis, and each has different capabilities and limitations (Garfield,
2009; Persson et al., 2009). BibExcel was chosen for this study due to its
high degree of flexibility in modifying and/or adjusting the input data
imported from various databases, including Scopus and Web of Science,
and its ability to provide comprehensive data analysis for use in a range
of network analysis tools, including Gephi, VOSviewer and Pajek
(Persson et al., 2009). We used BibExcel to perform some initial bibliometric and statistical analysis and to prepare the input data for additional network analysis in Gephi. Due to space constraints, we do not
report bibliometric analysis here. The original data source containing
the bibliographic information of the articles is in CSV format. We
transferred the CSV format to TXT format using a Python program to
create a suitable format to input into BibExcel. Citation information
such as author, title, journal, publication year, keyword, affiliation, and
Table 3
Inclusion and exclusion criteria in the refinement.
Inclusion criteria
Exclusion criteria
Focus on SCF
Supply chain management not related to
finance
Finance not related to supply chain
Peer-reviewed journal paper in
English
(7) More researches using empirical and case study methods
are needed.
(1) SCF of multi-tier supply chains and more complex forms of
trade credit policies need to be considered;
(4) More forms of financing mechanisms should be introduced.
(3) Scholars needs adjust and extend the research models for
SCF practice, and the function of SCF changes
(2) Assumptions of models can be relaxed
(5) Combining SCF with sustainable supply chain management
(6) Combining SCF with specific industry sectors
Non-English language journals
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X. Xu et al.
Number of papers
80
71
70
60
50
40
40
23 23 24
30
20
10
1 1 1 1 1 1 1 2 1 1 1 1 2 2
9
6
5 7
12
28
36
32
15
1970
1973
1977
1981
1984
1985
1986
1987
1994
1995
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
0
Fig. 1. Publishing trend in SCF (N = 348).
Table 4
Top 12 journals contributing to SCF (N = 348).
Table 5
Top 11 papers by local citation.
Journals
Total
Author (year)
Local citation
Global citation
International Journal of Production Economics (IJPE)
European Journal of Operational Research (EJOR)
Applied Mathematical Modelling (AMM)
International Journal of Systems Science (IJSS)
International Journal of Operational Research (IJPR)
Computers & Industrial Engineering (CIE)
International Journal of Information and Management Sciences (IJIMS)
Management Science (MS)
Journal of the Operational Research Society (JORS)
Asia-Pacific Journal of Operational Research (APJOR)
Yugoslav Journal of Operations Research (YJOR)
OPSEARCH
39
20
18
13
12
10
10
10
8
7
7
7
Goyal (1985)
Abad and Jaggi (2003)
Chung and Liao (2004)
Teng et al. (2005)
Chung et al. (2005)
Huang (2007)
Teng and Chang (2009)
Sarker et al. (2000)
Liao (2008)
Ho et al. (2008)
Shinn and Hwang (2003)
187
60
59
57
54
51
50
49
48
48
48
1309
300
233
288
189
209
189
350
176
153
250
Table 5 shows the top 11 of 348 papers based on the number of their
citations. A local citation analysis shows how many times a paper has
been cited by others within this 348-node network, while a global citation analysis provides the overall number of citations in all databases,
including citations from other disciplines and research areas.
reference was extracted from the papers identified.
3.1.2. Network analysis
A network analysis and graphical investigation was then applied to
the 348 papers. We used Gephi software to perform a citation analysis
and topical content-based classification of the existing literature of SCF.
Gephi was chosen for this study over existing network analysis software
such as Pajek (Batagelj and Mrvar, 2011) and VOSviewer (van Eck and
Waltman, 2013) due to its easy and broad access to network data, visualisation flexibility (editable and user-friendly environment), advanced filtering capabilities, specialised clustering of data, ability to
work with different data formats, and built-in network analysis toolboxes (Gephi, 2013; Bastian et al., 2009). To use Gephi to map and
visualise the citations among papers, a graph dataset needed to be
generated, with published papers shown as nodes and citations represented by the arcs/edges between the nodes. The bibliographic data
were reformatted with BibExcel to represent a graph dataset, which is
in “.NET” format, to allow for Gephi analysis. The network analysis
consists of citation and co-citation analyses, which are detailed in sections 3.2 and 3.3, respectively.
3.2.1. Journal quality analysis
We analysed paper quality based on the AJG 2015 journal guide.
Among the 348 papers, 14 papers were published in AJG Grade 4*
journals, which are world-leading journals; 24 papers were published in
Grade 4 journals; 82 papers were published in Grade 3 journals; 26
papers were published in Grade 2 journal and 35 papers were published
in Grade 1 journals. The other 167 papers were published in journals
with no rating. 52.01% of the papers were published in journals of
Grade 1 and above, and 34.48% of the papers were published in journals of Grade 3 and above.
3.2.2. PageRank analysis
In addition to citations, Ding et al. (2009) argue that “prestige” is an
important indicator of impact. Prestige can be measured by the number
of times a paper is cited by other highly cited papers. A highly cited
paper may not necessarily be a prestigious paper, although in some
cases there might be a strong positive correlation between the two
measures. PageRank (Brin and Page, 1998) can be used as a measure of
both popularity and prestige. PageRank was introduced to prioritise
web pages when a keyword search is performed in a search engine.
Although originally created to discover the connectivity of webpages,
PageRank can be extended to find the citation link between papers.
Consider paper A, which has been cited by other papers—namely, T1, …,
Tn , where paper Ti has citations C (Ti ) . In this case, the PageRank of
3.2. Citation analysis
In the past, different methods have been used to measure the significance of a publication. The most common method is a citation
analysis, which aims to determine the popularity of a publication by
counting the number of times a publication is cited by other publications (Cronin and Ding, 2011). A citation analysis for the 348 papers
revealed that 214 papers have cited others in this 348-node network.
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International Journal of Production Economics 204 (2018) 160–173
X. Xu et al.
pattern, which is unsurprising in light of the random nature of the
positioning. The nodes have identical sizes but different (x, y) coordinates.
paper A (denoted by PR(A) ) in a network of N papers can be calculated
as
PR(A) =
PR (T1)
PR (Tn ) ⎞
(1 − d )
+ d⎛
+…+
C (Tn ) ⎠
N
⎝ C (T1)
⎜
⎟
3.3.1. Literature classification: data clustering
The nodes of a network can be divided into clusters or modules in
which the connection (density of edges) is greater between the nodes of
the same cluster compared to those of different clusters (Clauset et al.,
2004; Leydesdorff, 2011; Radicchi et al., 2004). In a co-citation network, a cluster can be seen as a group of well-connected publications in
a research area with limited connection to publications in other clusters
or research areas. Data clustering (also termed modularity) has been
used in the past as a classification tool for grouping a set of given
publications (Radicchi et al., 2004). It allows for the topological analysis of a co-citation network, identifying topics, interrelations and
collaboration patterns. Data clustering has received increasing attention
from scholars and research organisations, turning it into a critical research field in social network analysis (Blondel et al., 2008).
The default clustering tool in Gephi is based on the Louvain algorithm, an iterative optimisation model that aims to determine the optimal number of partitions that maximise the modularity index (Blondel
et al., 2008). The modularity index of a partition is a scalar value between −1 and + 1 that measures the density of links inside communities versus the links between communities. According to Blondel et al.
(2008), for a weighted network (i.e., networks with weighted links,
such as the number of co-occurrence of two articles in the reference
list), modularity index Q can be calculated as
where parameter d is a damping factor between 0 and 1 that represents
the fraction of random walks that continue to propagate along the citations.
PageRank forms a probability distribution over papers, so the sum
of all papers' PageRank calculations will be equal to one. Using this
formula, PageRank is calculated based on an iterative algorithm and
corresponds to the principal eigenvector of the normalised citation
matrix of the papers. In the original Google PageRank algorithm of Brin
and Page (1998), the parameter d was chosen to be 0.85. This value was
prompted by the anecdotal observation that an individual surfing the
web will typically follow approximately six hyperlinks, corresponding
to a leakage probability of 1/6 ∼ = 0.15 = (1 − d), before becoming
either bored or frustrated with the search and beginning a new search.
In the context of citations, the entries in the reference list of a typical
paper are collected following somewhat shorter paths of average length
2 (Chen et al., 2007), making the choice d = 0.5 more appropriate for a
similar algorithm applied to the citation network.
For the 214 papers identified from co-citation analysis (detailed in
the next section 3.3), PageRank values vary between 0.0009 and
0.0164. When we focus on the journals of AJG Grade 3, 4 and 4*, the
top 10 papers based on a PageRank measure are shown in Table 6. It
can be seen that a higher number of local and global citations cannot
guarantee the prestige of a paper. For example, Chung and Liao (2004)
is a high-ranked paper by citations (ranks the third in Table 5), but only
lists tenth as a prestigious paper in Table 6. Equally, there are prestigious papers that are not highly ranked (e.g., Jaggi et al., 2008;
Buzacott and Zhang, 2004).
Q=
where Aij represents the weight of the edge between nodes i and j, ki is
the sum of the weights of the edges attached to node i ⎛⎜ki = ∑j Aij⎟⎞, c i is
⎝
⎠
the community to which node i is assigned, δ(u, v) equals 1 if u= v ; and
1
equals 0 otherwise, and m= 2 ∑ij Aij, which represents the sum of the
weights of all the edges, as every edge is calculated twice when we add
up all the Aij.
To get the optimal value efficiently, the Louvain algorithm repeats
two phases below iteratively. First, each node of the network is assigned
to a different community. For each node i , the gain of modularity is
evaluated by removing i from its community and by placing it in the
community of each neighbour j of i . Second, node i is placed in the
community for which this gain is maximum if the gain is positive or i
stays in the original community if no positive gain is possible (Blondel
et al., 2008). The first phase stops when a local maximum of the
modularity is attained. Applying this algorithm to the filtered 214-node
co-citation network in Gephi resulted in the creation of four clusters.
The number of papers in each cluster varies from 31 articles for
Cluster two to 63 articles each for Clusters one and three, the largest
modules (Table 7). To capture the high-quality papers for the content
analysis (next section 4), we only chose papers published in journals of
AJG Grade 3, 4 and 4* due to their generally higher academic rigour
and quality. Overall, 96 papers out of the 214 were selected for further
clustering and content analysis. Among them, 34 papers fall into Cluster
one, 11 papers fall into Cluster two, 25 papers fall into Cluster three,
and 26 papers fall into Cluster four.
To determine the research focus for each cluster, we needed to
identify the lead papers (top 10 papers) in each cluster (Table 8). This is
a common practice in other bibliometric analysis papers (e.g., Fahimnia
et al., 2015). The lead papers provide a general description of each
cluster. A PageRank measure was used for this purpose. In a co-citation
network, the PageRank algorithm takes into account how many times a
paper is co-cited with other papers (the popularity measure) and how
many times it is co-cited with highly co-cited papers (the prestige
measure). Most of the papers in this study that have a high PageRank
3.3. Co-citation analysis
A co-citation network consists of a set of nodes representing journal
articles and a set of edges or links representing the co-occurrence of the
nodes (articles) in other papers (Leydesdorff, 2011). Therefore, two
publications are considered to be co-cited if they appear together in the
reference lists of other documents. Papers that are more often cited
together are more likely to present similar subject areas or be related
(Hjørland, 2013). The co-citation map visualisation is a form of exploratory data analysis (EDA) that relies on graph theory to explore
data structure (Pampel, 2004). The initial co-citation mapping with
Gephi revealed that 214 of the 348 articles have been co-cited by other
papers within this sample. We use these 214 papers for the further
Gephi data-clustering analysis. When opening the ‘.NET’ file in Gephi
for the first time, the positioning of the nodes in the co-citation map is
randomly generated by the software. This layout has no discernible
Table 6
Top 10 papers by PageRank.
Author (year)
PageRank
Local citation
Global citation
Goyal (1985)
Jaggi et al. (2008)
Huang (2007)
Abad and Jaggi (2003)
Teng and Chang (2009)
Buzacott and Zhang (2004)
Huang and Hsu (2008)
Liao (2008)
Ho et al. (2008)
Chung and Liao (2004)
0.01638354
0.01065293
0.01061392
0.01048360
0.01030263
0.01016512
0.01007087
0.00999925
0.00972325
0.00964314
187
46
51
60
50
37
47
48
48
59
1309
205
209
300
189
579
156
176
153
233
ki kj
1
∑ ⎡Aij − 2m ⎤⎥ δ(ci, cj)
2m ij ⎢
⎣
⎦
(Note: Local citation: citation within the 348 papers; Global citation: citation in
all databases).
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X. Xu et al.
Table 7
Literature classification: the primary research clusters (N = 214).
(Note: selected papers refer to papers published in journals of ABS Grade 3, 4 and 4*)
Cluster
No. of papers
No. of selected papers
Area of research focus
1
2
3
4
Total
63
31
63
57
214
34
11
25
26
96
Deteriorating inventory model under trade credit policy based on EOQ/EPQ model
Inventory decision with trade credit policy under more complex situations
Interaction between replenishment decisions and delay payment strategies in the supply chain
Roles of financing service in the supply chain
3.3.2. Analysis of the research clusters
As discussed in section 3.3.1, four research clusters have been
identified with co-citation analysis. To identify the area of research
focus of each cluster, we analysed and evaluated the contents of the top
10 papers of each cluster shown in Table 8. Cluster one focuses on the
deteriorating inventory model under trade credit policy. This cluster is
focused on the impact of trade credit policy on the optimal ordering or
production quantity based on the classical EOQ or EPQ model, respectively. Cluster two is about the inventory decisions made with
trade credit policy and was primarily developed from cluster one. It
examines how trade credit policy impacts optimal ordering/product
quantity within the EOQ or EPQ framework but tends to extend the
traditional model or assumptions. Cluster three is a transition from
Clusters one and two to Cluster four. It focuses on the interaction between replenishment decisions and delay payment strategies in the
supply chain. This cluster begins to study the impact of SCF throughout
the supply chain. Cluster four is mainly focused on the roles of financing service in the supply chain. This cluster investigates how
formal or informal financing services (e.g., bank credit or trade credit)
affect joint operational and financial decisions in a supply chain and
show whether the SCF coordinates the supply chain or creates value for
individual companies or the entire supply chain.
Table 8
The lead papers using a PageRank measure.
Cluster 1
Cluster 2
Goyal (1985)
Abad and Jaggi (2003)
Liao (2008)
Ho et al. (2008)
Chung and Liao (2004)
Ouyang et al. (2009)
Huang (2007)
Teng (2009)
Haley and Higgins (1973)
Chung et al. (2005)
Huang (2007)
Teng and Chang (2009)
Huang and Hsu (2008)
Mahata (2012)
Chang et al. (2010)
Kreng and Tan (2011)
Su (2012)
Thangam (2012)
Mahata and Goswami (2007)
Soni and Patel (2012)
Cluster 3
Cluster 4
Jaggi et al. (2008)
Chen et al. (2014b)
Chern et al. (2013)
Wu et al. (2014)
Zhou et al. (2013)
Ouyang and Chang (2013)
Wang et al. (2014)
Tsao and Sheen (2008)
Chen et al. (2014a)
Zhou et al. (2012)
Buzacott and Zhang (2004)
Raghavan and Mishra (2011)
Lee and Rhee (2011)
Kouvelis and Zhao (2012)
Gupta and Wang (2009)
Jing et al. (2012)
Chen (2015)
Protopappa-Sieke and Seifert (2010)
Chen and Wang (2012)
Zhang et al. (2014)
3.3.3. Dynamic co-citation analysis
To understand the evolution of SCF research over time, we also
completed a dynamic co-citation analysis for the papers of all clusters,
which shows the evolution/development of clusters over time. Table 10
shows the number of papers published in each cluster since 1973. The
earlier publications are more focused on Cluster one. Research on
cluster one appeared in the 1970s and developed steadily but declined
from 2014 onwards. The numbers of articles for Clusters three and four
began to grow nearly at the same time, in 2005 and 2004, respectively.
Cluster two emerged the latest (in 2007) and declined from 2014 onwards. Clusters one and two stopped developing in recent years while
Clusters three and four continue to grow (Table 10).
Note: only papers published in journals of ABS Grade 3, 4 and 4* are included.
also have a high citation count.
Identifying the contributing journals for each research cluster can
help determine the most relevant journal outlets in each area. Table 9
shows the key contributing journals for each cluster. We find that IJPE
contributes the most to three out of the four clusters (the only exception
being Cluster four). EJOR ranks the second in contributing journals
based on the number of papers in the four clusters.
Table 9
Key contributing journals in each research area (N = 214).
Clusters
Top journals
Number of papers
1. Deteriorating inventory model under trade credit policy based on EOQ/EPQ model
International Journal of Production Economics
European Journal of Operational Research
Journal of the Operational Research Society
Computers & Operations Research
Production Planning & Control
International Journal of Production Economics
European Journal of Operational Research
Expert Systems with Applications
International Journal of Production Economics
European Journal of Operational Research
Computers & Operations Research
Journal of the Operational Research Society
Management Science
European Journal of Operational Research
International Journal of Production Economics
Operations Research
Production and Operations Management
15
7
4
3
2
5
2
2
12
6
2
2
5
5
5
2
2
2. Inventory decision with trade credit policy under more complex situations
3. Interaction between replenishment decisions and delay payment strategies in the supply chain
4. Roles of financing service in the supply chain
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papers (Table 7) from the co-citation analysis plus the 16 papers published in the most recent three years. Overall, 112 papers (all published
in AJG 3, 4 and 4* journals) were reviewed for content analysis.
Table 10
The number of published papers in each cluster (1973–2016).
Year
Number of published articles
Cluster 1
1973
1981
1985
1986
1987
1994
1995
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Total
Cluster 2
Cluster 3
4.1. Cluster one: deteriorating inventory model under trade credit policy
based on EOQ/EPQ model
Cluster 4
1
1
1
1
1
1
1
1
1
2
2
3
5
8
3
8
5
5
5
2
3
3
1
63
With 34 papers, Cluster one is the largest cluster and emerged as
early as 1973. This cluster focuses on the impact of specific forms of
financing mechanisms on decisions of the classical inventory model
(Haley and Higgins, 1973; Goyal, 1985; Jaggi and Aggarwal, 1994;
Abad and Jaggi, 2003; Liao, 2008; Ho et al., 2008). The papers in this
cluster have similar research frameworks. First, and more specifically,
almost all the research objectives of these papers are perishable products or deteriorating inventory systems. Second, the financing mechanisms that these papers study are all trade credit policies in the
forms of permissible delays in payment, discounted cash flows (DCF)
and date terms (Robb and Silver, 2006). Third, inventory decisions are
focused on the optimal price, ordering quantity or replenishment frequency. These models are all based on traditional EOQ or EPQ models.
One empirical paper in Cluster one analyses the firm-level data of
five African countries from the survey administered by the Regional
Program on Enterprise Development (RPED) at the World Bank
(Fisman, 2001). That paper found that the supplier credit is positively
correlated with capacity utilisation i.e., firms lacking trade credit are
likely to face inventory shortages, which lead to lower rates of capacity
utilisation (Fisman, 2001). Papers within Cluster one have strong connections with one another.
2
1
1
2
1
6
3
6
7
5
1
31
3
5
2
9
14
14
9
6
63
2
9
4
8
7
4
5
8
6
57
This chronological evolution of the four clusters is also shown graphically in Fig. 2. One node represents one article and the size of a node
for each layout represents the PageRank score of the article. Hence, the
larger the size of a node, the more highly cited and prestigious the
corresponding paper. The links between the nodes represent the citation rates: the thicker the links, the closer the clusters. It can be seen
that Cluster one had many papers published between 1973 and 2013
but has gone silent since 2013. Cluster four did not catch up until 2007
after which the number of its papers has increased significantly. Cluster
three emerged and has grown steadily since 2005. Cluster two has a
relatively smaller number of papers, emerged in 2007 and has declined
since 2013.
Additionally, we observe that Clusters two, three and four have
grown since approximately 2008. The reason for this may lie in the
rapid development of literature on supply chain management in the late
2000s. We refer the readers to de Kok and Graves (2003) and SimchiLevi et al. (2004) for a general overview of supply chain management.
Supply chain management is focused on the coordination of material,
information, and capital flows, and therefore it is logical for scholars to
start examining the effects of joint financial and operation decisions of
firms on their supply chain management (Babich and Sobel, 2004;
Buzacott and Zhang, 2004). As a result, the literature on SCF has grown
rapidly since 2008.
4.2. Cluster two: inventory decision with trade credit policy under more
complex situations
With only 11 papers, Cluster two is the smallest cluster and emerged
the latest, in 2007. This cluster develops from Cluster one and includes
further study of the impact of trade credit policy on inventory decisions.
It is focused on deteriorating items and considers inventory decisions
within the EOQ or EPQ framework, but extends the traditional model
and assumptions compared with Cluster one in two ways. The first way
in which this cluster extends the traditional model and assumptions is
to extend the classical EOQ or EPQ models by introducing new methods
such as fuzzy sense into the modelling and considering the demand rate,
holding cost, ordering cost and purchasing cost as fuzzy numbers
(Mahata and Goswami, 2007). Some other papers extend the model in
terms of trade credit by considering two-level trade credit instead of
one-level trade credit (Teng and Chang, 2009; Chang et al., 2010). The
second way in which this cluster extends the traditional model and
assumptions is to extend the assumptions and consider extreme cases.
For example, some papers assume that retailers can obtain the full trade
credit offered by suppliers while they offer partial trade credit to their
customers (Huang and Hsu, 2008; Mahata, 2012); other papers consider
extreme scenarios in which suppliers offer retailers a partially permissible delay in payments when the order quantity is smaller than a
predetermined quantity (Huang, 2007a).
4. Content analysis of the four clusters
4.3. Cluster three: interaction between replenishment decisions and delay
payment strategies in the supply chain
To obtain insights into each cluster, a content analysis was conducted to identify the research sub-themes within each of the four
clusters identified from co-citation analysis. Since co-citation analysis
considers only co-cited papers, recent articles tend to be missed due to a
low number of citations. To perform the content analysis, we screened
papers that were published in 2014, 2015 and 2016, but not included in
the 214-node co-citation network. Overall, 90 papers were found to be
published in these three years outside the 214-node and 16 papers of
that 90 were published in AJG Grade 3, 4 and 4* journals. We found
that the 16 papers fit in the existing clustering structure and fall into
Clusters 2, 3 and 4. Finally, the content analysis is based on the 96
Cluster three consists of 25 articles and began in 2005. This cluster
represents a transition from Clusters one and two to Cluster four. It
focuses on the interaction between replenishment decisions and delayed payment strategies in the supply chain. Because Cluster three
developed earlier than Cluster two, it not only contains similar topics
(e.g., replenishment/ordering/inventory decisions) to those contained
in Clusters one and two and extends the classical EOQ or EPQ models
(Chung et al., 2014) but also begins to consider various forms of supply
chain-level financing mechanisms that are totally different from those
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Fig. 2. The evolution of SCF research over time (i) 1973–2006, (ii) 1973–2008, (iii) 1973–2010, (iv) 1973–2012, (v) 1973–2014, (vi) 1973–2016.
permissible delay period at the full price. However, Zhou et al. (2013)
assume that the retailer may pay for any fraction of the ordered items
within a short permissible delay period to receive a cash discount and
then the remaining is paid within a long permissible delay period,
which offers more flexible payment choices to retailers in reality. Furthermore, it adds new assumptions such as uncertain investment opportunity, varying deterioration and limited storage space (Marchi
et al., 2016; Sarkar et al., 2015; Zhong and Zhou, 2013). It also considers imperfect situations, e.g., the items are of imperfect quality and
the production processes are imperfect (Ouyang and Chang, 2013).
Third, it tends to consider new research ideas such as cost and credit
risks (Wu et al., 2014) and the coordination of the supply chain in the
form of supply chain contracts (Kouvelis and Zhao, 2016), combining
considered in Clusters one and two but are closer to those considered in
Cluster four, e.g., it focuses on the entire supply chain instead of a
single player and SCF mechanisms other than trade credit. Thus, we
conclude that Cluster three represents a transition from Clusters one
and two to Cluster four.
More specifically, this cluster has three main features compared to
Clusters one and two. First, it extends the traditional replenishment
models, which are focal company focused, by considering supply chain
players and trade credit stages and constructing a Stackelberg game
(Zhou and Zhou, 2013). Second, it relaxes some strict assumptions. For
example, one commonly used assumption is that the retailer either pays
for all the ordered items within a short permissible delay period to
receive a cash discount or pays for all the ordered items within a long
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these ideas with supply chain finance.
chain performance, e.g., the coordination of entire supply chains.
Kutsuna et al. (2016) study financial and supply-chain data for private
firms from the Cosmos database provided by Teikoku Databank
(https://www.tdb.co.jp/english/news_reports/t030918.html), which
contains financial reports and firm-attribute data for each firm's top 5
suppliers and customers. They examined the effects of Initial Public
Offering (IPO) on revenue growth, PP&E (property, plant and equipment), accounts receivable, and the cash and loans of different supply
chain players.
Birge (2015) studies the relationship between operations management and financial management in which financial activities have a
significant impact on operations and operation management provides
new perspectives on financial decisions. He provides examples and
empirical observations about the relationship between operations and
finance under the conditions of the absence of arbitrage, the differences
between systematic and idiosyncratic risk, the valuation of limited
production resources and imperfect market assumptions (Birge (2015)).
In addition, adopting a case study method, Caniato et al. (2016)
analyse 14 cases involving the application of SCF solutions among
Italian companies and provide a reference framework in which under
the two objectives (improving the adopter's financial performance
(objective 1) vs. securing the upstream/downstream supply chain's financial performance (objective 2)), different factors lead to the adoption of different SCF solutions. For objective 1-driven SCF solutions, the
factors that affect successful adoption include the level of trade process
digitalisation (negatively related), inter-company collaboration and the
bargaining power towards supply chain players; for objective 2-driven
SCF solutions, the factors leading to adoption include intra-company
collaboration, level of trade process digitalisation and the financial
attractiveness of the focal companies (Caniato et al. (2016)).
4.4. Cluster four: roles of financing service in supply chains
With 26 papers, Cluster four is the second largest cluster. The earliest article in Cluster four was published in 1981, but the number of
papers for this cluster then remained stagnant until 2004. This cluster
developed rapidly, especially after 2008 and is primarily focused on the
impact of integrated operations and financing decisions on supply chain
performance.
This cluster also studies the roles of financing service providers in
the supply chain, i.e., how they innovate and transform SCF. First, it
investigates how formal or informal financing services, e.g., bank credit
and trade credit, affect joint operational and financial decisions in a
supply chain. Some papers in this cluster compare trade credit with
bank credit (Chen, 2015). Second, this cluster considers not only trade
credit but also other forms of supply chain financing mechanisms, e.g.,
reverse factoring (van der Vliet et al., 2015) and different supply chain
contracts (Lee and Rhee, 2010). Cluster four pays more attention to
supply chain coordination and examines how supply chain financing
service providers coordinate the supply chain or creates value for both
individual companies and the entire supply chain. Third, the models in
this cluster can be applied to more complicated situations. This cluster
stresses the budget-constrained conditions of supply chain players and
introduces new conditions of tariff or fluctuated demand information or
bankruptcy (Shang et al., 2009; Yan and Sun, 2013). Finally, we notice
that unlike Clusters one, two and three, which almost exclusively adopt
the modelling method, more diverse methods were adopted in Cluster
four, including surveys, experiments (e.g., Chen et al., 2013) and a
combination of empirical and modelling methods (e.g., Ferris, 1981;
Bougheas et al., 2009).
Two papers in Cluster four used a mixed method of modelling and
empirical methods. Ferris (1981) develops a theory of trade credit
adopted by supply chain partners to decrease the transaction costs
arising out of trading uncertainty and then used data collected from the
Internal Revenue Service publication Statistics of Income: Corporate
Income Tax Returns, Total Active Returns (in the US) for the years
between 1945 and 1972 to test his theory. In our analysis, this paper is
the earliest published article to use the empirical method in SCF research. Bougheas et al. (2009) develop a model that recognises the
motivation for a firm to offer and receive trade credit, which in turn
affects inventory and profitability. To validate their model, they use the
profit and loss balance sheet data gathered by Bureau Van Dijk Electronic Publishing in the FAME database (https://www.bvdinfo.com/engb/home) and focus on firms in the manufacturing sector from 1993 to
2003 (Bougheas et al. (2009)).
5. Discussion and future research directions
Cluster one appeared first, in 1973, and Cluster two emerged last, in
2007, but both clusters declined beginning in 2014. From 2008 onwards, there was a boost in Clusters three and four. SCF research has
gradually developed into two main streams. One stream consists of
Clusters one and two, which represents the traditional SCF research
with a focal company focus. Cluster two was developed from Cluster
one. These two clusters are focused on how financial mechanisms affect
operation decisions through inventory models, especially classical EOQ
and EPQ models and their variations within a firm's boundary.
The other stream is represented by Cluster four, which examines
how financial mechanisms influence the operation decisions in the
entire supply chain and focus more on how SCF services create value for
the supply chain, usually under conditions of uncertain demand and
information asymmetry. The focus of the second stream (i.e., financing
in the supply chain) represents a step change from the traditional internal operations management view of financing within a firm's
boundary to a supply chain view of financing going beyond a firm's
boundary. Cluster three is a transition from the first stream to the
second.
The following key conclusions can be drawn from the findings obtained from bibliometric, network and content analyses:
4.5. Papers published in the recent three years
In this section, we present additional insights and trends identified
from the review of the 16 papers published in 2014, 2015 and 2016 and
not included in 214 papers from the co-citation analysis. The 16 papers
primarily fall into Clusters two, three and four and contain six empirical
studies (out of 10 overall). Some empirical papers examine the factors
affecting the SCF solution. Fabbri and Klapper (2016) use a novel firmlevel database of Chinese firms to investigate how the supplier's bargaining power affects trade credit. Houston et al. (2016) analyse an
extensive dataset that captures the supply chain relationships of
bankrupt firms over the period of 1990–2009 and examine how a firm's
bankruptcy affects the bank financing costs of its key suppliers. They
find that the borrowing costs of key suppliers significantly increased in
the aftermath of their client firm's bankruptcy. Alan et al. (2014) analyse a sample of publicly listed US retailers during the period of
1985–2010 and find that inventory productivity strongly predicts future stock returns.
Some other empirical papers examine the effects of SCF on supply
(1) A frequency analysis showed that SCF research has had a long research history since 1973, but it has gained momentum and started
growing rapidly especially in and after 2008.
(2) After filtering the papers based on the journal quality, 82.14% of
the 112 articles are published in 8 major journals. IJPE and EJOR
published the most number of SCF papers with relatively high
quality among all journals.
(3) SCF research can be classified into two main streams comprising
four clusters. Research stream two (Cluster four) develops from
research stream one (Clusters one and two). Cluster three is a
transition from the first stream to the second. Cluster four
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Table 11
Identifying research directions in SCF research.
Category
Gap/issue
Research direction
Number of SCF levels
Assumption for modelling
Models and functions of SCF
Focus on one-level or two-level trade credit
Existing assumptions are strict.
1. Certain demand is limited.
2. Only considering cost reduction
3. Limited forms of models
4. Only as a marketing tool
Forms of financing
Link to SCM topics
Industry sector
Research method
Lack of financing forms
Few papers study the sustainable SCF.
Few papers consider SCF in a specific industry.
Dominated by modelling
Studying multi-level SCF
Relaxing existing assumptions and introducing new assumptions
1. Considering more forms of demand
2. Considering profit increase
3. Adopting game theory
4. Working as a coordination tool
5. Considering co-operation
Studying the impact of tax or exchange rates on SCF
Relating sustainability with SCF
Studying SCF in agricultural sectors
Calling for empirical analysis e.g. case study, statistical analysis
financial market are risk-neutral. In reality, however, different firms
have different attitudes towards risk. Tang and Musa (2011) literature
survey points out that financial risk is one of the major risk issues in
supply chain research. According to Heckmann et al. (2015), the subjective perception of the importance of risk is divided into three groups:
risk-averse, risk-seeking, and risk-neutral. Risk attitudes have a decisive
influence on the measurement of future supply chain performance and
consequently co-determine supply chain decisions (Heckmann et al.
(2015)). In Cluster four, researchers begin to investigate the SCF in riskaverse cases (Babich and Sobel, 2004; Yan and Sun, 2013; Zhang et al.,
2014). Kouvelis and Zhao (2012) build their basic model with the riskneutral assumption and extend the model to a scenario in which there is
a risk-averse retailer. They find that the risk-averse retailer can order a
larger quantity and obtain larger expected utility under supplier financing when the supplier's interest rate equals the bank's, which
means that supplier financing is preferable over bank financing for a
retailer regardless of its risk preferences (Kouvelis and Zhao (2012)).
However, Heckmann et al. (2015) find that there is a lack of research
focusing on the effects of different attitudes towards supply chain risk
on supply chain decision-making. The same conclusion can be drawn
from our content analysis of the four clusters. Interestingly, none of the
reviewed paper across the clusters examine the risk-seeking assumption. Future research may consider assumptions based on different attitudes towards risk, which will lead to different operational and financial decisions in SCs (Chen and Wang, 2012).
Third, with the rapid development of SCF practice, scholars will
need to adjust and extend the research models to adapt to a more
turbulent environment for SCF. This is reflected in the following aspects:
represents a new and emerging research area in SCF, emphasising
financing service and exploring the value creation for the whole
supply chain, which deserves more attention. Based on the bibliometric and content analyses, we propose seven future research
directions for SCF (Table 11).
First, existing studies tend to investigate either one-level or twolevel trade credit. Cluster one focuses on the one-level trade credit and
Cluster two develops the model based on Cluster one and considers the
two-level trade credit so that the retailer can obtain the full trade credit
offered by the supplier and the retailer just offers the partial trade credit
to customers. There is a need to go beyond one or two levels and to look
into the SCF of multi-tier supply chains. In Cluster three, Pal et al.
(2014) investigate the optimal replenishment lot size of the supplier
and optimal production rate of the manufacturer under three levels of
trade credit policy in which the supplier provides a fixed credit period
to the manufacturer while the manufacturer gives a fixed credit period
to the retailer, who in turn offers a credit period to each of its customers. In the future, scholars may focus on the multi-level trade credit or
even SCF in a supply network.
Additionally, from the papers across the clusters, we found a few
limited forms of trade credits. Usually, the papers consider partial or
full trade credit, e.g., permissible delay in payment, discounted cash
flows (DCF), and date terms. In the future, SCF scholars may consider
more complex forms of trade credit policies and may assume that the
credit amount or period offered by suppliers is based on the order
quantity of retailers. More specifically, SCF researchers may study
whether the conclusions in the one- or two-level SCF will stand in the
multi-level situation or how the conclusions will change from the original forms of trade credit to more complex forms.
Second, some assumptions for SCF research are strict and may be
relaxed and new assumptions may be introduced. For example, all the
existing SCF research papers across Clusters one to three have the assumption of symmetric information, i.e., all the players in the supply
chain have equal information about financing. In reality, however, this
may not be the case, e.g., more powerful firms in the supply chain tend
to have more financial information than others. In Cluster four, more
papers consider the asymmetric information assumption (Daripa and
Nilsen, 2011; Jing et al., 2012; Cai et al., 2014). Shang et al. (2009)
examine three information scenarios: echelon, local, and quasilocal. In
the echelon scenario, each supply chain player can access the inventory
and cost information within its echelon, which includes the player itself
and all downstream players, including customer demands; in the local
scenario, each local supply chain partner accesses only local information, i.e., its own inventory and cost information; in the quasi-local
scenario, all customer-demand information is shared by the supply
chain players, but not cost information is not. Future research could
consider how financing adds value to the entire supply chain under the
condition of information asymmetry.
Another example is that nearly all of the papers except for one (i.e.,
Birge, 2015) in Clusters one to three assume that all the players in the
1) The assumptions of certain demand considered in EOQ or EPQ
models do not match reality. Most of the papers in Clusters one and
two, especially in Cluster one, adopt the classical EOQ or EPQ
models considering that demand is certain. As the research and
application of SCF develop, the theoretical analysis of inventory
under certain demand cannot satisfy the sophisticated demand
found in reality. In the future, more flexible forms of demand or
fluctuated demand information should be considered for a broader
scope of application. For example, Ouyang et al. (2009), in Cluster
one, suggest that future research could assume that demand is a
function of selling price and varying time. Cai et al. (2014), in
Cluster four, argue that demand could be a random variable following a cumulative distribution function.
2) According to all the papers in Clusters one and two, optimal decisions are made based on the cost of SCF using EOQ or EPQ models
(e.g., Chen and Kang, 2007, Cluster one). However, the aim of
adopting SCF could be not only to reduce the cost of the supply
chain but also to increase the revenue of supply chain players. Zhou
et al. (2012), in Cluster three, consider a two-echelon supply chain
in which the supplier hopes to incentivise the retailer to order more
items by offering trade credit. They show that trade credit policy
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researchers could focus on how co-opetition between players affects
SCF or even co-opetition in finance at the supply network level.
Researchers may investigate how co-opetitive dynamics change as
firms allocate financial resources and respond to environmental
changes and the actions of other firms over time (Pathak et al.,
2014).
could increase not only the overall chain profit but also each
member's profit under some conditions (Zhou et al. (2012)). In the
future, more research focusing on both cost reduction and profit
generation by the supply chain players should be conducted.
3) Without being limited to the classical inventory models and theories, different models and theories may be considered. For example, scholars tend to adopt game theory to model the decisions of
supply chain players engaging with SCF and compare the bargaining
power of each player. Caldentey and Haugh (2009), in Cluster four,
study the performance of a stylised supply chain in which two firms
compete in a Stackelberg game and consider the payoff of the retailer and producer. In their paper, the producer acts as a Stackelberg leader and offers a fixed wholesale price; the retailer acts as a
follower and determines the purchasing quantity. Guan et al.
(2016), in Cluster four, consider a two-stage Stackelberg game between the assembler and the two suppliers, in which the assembler
is a leader and initially chooses the contract type and the buffer
time, and the two suppliers are followers and simultaneously determine their production lead times. These studies suggest that more
game theory models and their variations can be used to analyse the
SCF problems in further research; examples include the Bertrand
duopoly model or dynamic games with complete or incomplete information.
4) One important transition involves the transformation of the function
of SCF from a marketing tool to a coordination tool. Clusters one and
two study the change of inventory or replenishment policies with or
without trade credit. Some papers find that trade credit increases
optimal replenishment quantities and others do not (e.g.,
Daellenbach, 1986; Cluster one). In this way, trade credit is considered a marketing tool in these two clusters. However, scholars
now tend to shift their attention towards the coordination of upstream and down-stream supply chain players instead of the inventory decisions or replenishment policies of a single player. In
Clusters three and four, we find that more papers began to consider
supply chain coordination contracts, thus demonstrating that trade
credit plays an important role in coordination and incentive mechanism of supply chain and works as a coordination tool. For instance, Lee and Rhee (2011), in Cluster four, consider coordination
contract, employ a buyback/markdown allowance contract, and
derive the optimal markdown allowance and risk premium in the
trade credit for full coordination. This shows that trade credit is an
instrument for supply chain coordination (Lee and Rhee (2011)).
Yan et al. (2016), in Cluster four, design a partial credit guarantee
contract for SCF that incorporates the bank credit financing and
manufacturer's trade credit guarantee to analyse its equilibrium financing strategies. They find that the partial credit guarantee contract may realise profit maximisation and channel coordination in
the SCF system and achieve the super-coordination effect with a
suitable guarantee coefficient (Yan et al. (2016)). In the future, more
SCF research can be done by combining different contracts with
trade credit to achieve the coordination of the supply chain. Scholars can also design contracts combining the advantages of different
types of credit to better coordinate the whole supply chain.
5) Co-opetition in SCF deserves more attention from researchers.
According to Pathak et al. (2014), the concept of co-opetition, or
simultaneous competition and cooperation, originated from the
game theory literature and is a network-level phenomenon. They
specify four supply network archetypes that cover a wide range of
economic activities: Community, Federation, Consortium and Hierarchy. They then explain how co-opetitive relationships may evolve
in these supply network archetypes (Pathak et al. (2014)). As discussed previously, the focus of SCF research turns to the combination of game theory and SCF, and scholars should do more work on
the multi-level trade credit or even SCF in a supply network. Additionally, combining contracts and trade credit provides opportunities for supply chain collaboration and competition. In the future,
Fourth, papers in Cluster four have examined complicated situations, e.g., considering budget-constrained conditions, and introduce
new conditions of tariffs or bankruptcy (e.g., Yan et al., 2016), assuming that the retailer may have bankruptcy risk. In addition to existing forms of trade credit policy studied across the clusters, more
forms of financing mechanisms such as taxes should be introduced to
SCF research. For instance, future research could study the effects of tax
and exchange rates on SCF in a global supply chain context. Brick and
Fung (1984) and Qin et al. (2015) study the effect of taxes on the trade
credit decision and replenishment policies, but generally, there are few
papers of this kind. It is interesting to study how taxes influence the
decisions of governments and supply chain players. Additionally, there
are different tax codes such as sales tax and value-added tax. According
to Consumption Tax Trends (2016), 166 of the world's approximately
193 countries have adopted a VAT system. Taking the two biggest
economies in the world as examples, the United States has used a sales
tax while China conducted a tax reform that transitioned from sales tax
to value-added tax in 2016. Scholars may study the effects of different
tax codes on the entire supply chain so that researchers and policy
makers can obtain a better understanding of whether a tax reform is
needed. Especially with globalisation, more research is needed on the
global supply chain, and the topics of exchange rates will become
salient in SCF. Hsu and Zhu (2011) study the impacts of a set of Chinese
export-oriented tax and tariff rules on the optimal supply chain design
and operations for a firm that produces its product in China and sells it
in markets both inside and outside China. Kazaz et al. (2005) examine
the impact of exchange rate uncertainty on the choice of various optimal production policies and the conditions that lead to them. Their
model identifies the value of financial hedging for each firm rather than
a single price for all firms (Kazaz et al. (2005)). In the future, scholars
may consider the application of SCF in global supply chains and examine the impact of exchange rate or tariff rules on the SCF decisions of
supply chain players. Researchers could provide more ideas about how
to improve the profit and efficiency of the global supply chain with SCF,
helping firms make better import and export decisions and offering
suggestions to policy makers.
Fifth, we found that SCF papers across the four clusters tend to be a
niche since the link to other SCM topics is lacking. Increasing attention
is being paid to sustainable development and sustainable supply chain
management. Suppliers and retailers have begun to care about the environmental and social sustainability of their supply chains. For example, Nestlé was the world's largest food and beverage company in
terms of revenue in 2015 and has adopted the “creating shared value”
principle as its sustainability strategy. Nestlé purchases fresh milk from
dairy farmers and helps them upgrade and modernise their dairy farms.
Lack of capital is a big challenge for dairy farmers. To tackle this, Nestlé
collaborated with local governments and local banks to provide farmers
with financial support (Gong et al., 2017). However, few papers examine the relationship between sustainable development and SCF. Future research could examine the effects of the adoption of sustainability
criteria in the supply chain on SCF or explore how SCF promotes supply
chain sustainability. For example, Dye and Yang (2015), in Cluster
three, study sustainable trade credit and replenishment decisions under
carbon emission regulations. The combination of sustainability and SCF
may become a fertile area for SCF research.
Sixth, combining SCF with specific industry sectors or real problems
would add value to SCF research. SCF in specific industry sectors, such
as the manufacturing sector or the retail sector, deserves more attention
from SCF researchers. Specifically, there is very little research on SCF in
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X. Xu et al.
6. Conclusions
the agricultural sectors. Obaidullah (2015) reviews the principles,
modes and models of Islamic agricultural finance targeted at smallholder farmers and adopts a case study method to examine several
modes of Islamic agricultural finance for the rural poor. Among them,
credit-based and sharing-based modes work well under specific conditions, and there is no one-size-fits-all solution for financing the rural
poor. Chen et al. (2015) conduct a review of the existing literature on
agricultural value chains and their financing mechanisms in China and
India. They find that the internal financing of value chains (in terms of
provision of inputs, technology and services) plays a dominant role in
both countries and that value chain finance from commercial banks and
other financial institutions is limited and primarily occurs through tripartite agreements among financing institutions, lead firms and farmers
(Chen et al. (2015)).
Boyabatlı et al. (2011) examine the integration of long-term and
short-term contracting (i.e., a form of SCF) in the beef supply chain.
They analyse the optimal decision of the meat-processing company in
the beef supply chain and focus on the structure of optimal sourcing
portfolios between spot sourcing and long-term contracts. Here, they
analyse window contracts, with the contract price equal to a linear
function of the spot price but capped by upper and lower limits on the
realised contract price. Their paper shows that the meat-processing
company benefits from a low correlation between the spot price and
product market uncertainties, and this conclusion is independent of the
form of the window contract (Boyabatlı et al. (2011)). Using a calibration based on the GIPSA (2007) report, this paper elucidates for the
first time the value of long-term contracting as a complement to spot
sourcing in the beef supply chain (Boyabatlı et al. (2011)).
In reality, crop failure and market volatility may bring significant
risks to SCF. The practice of SCF in agricultural sectors develops
quickly, and many governments, such as China, publish policies to
promote and support agricultural SCF. However, the existing SCF research in agricultural supply chains is fairly rudimentary, e.g., few financing service tools have been applied to the agricultural supply
chain. All players in the agricultural supply chain need to have a better
understanding of underlying financing risks and mitigating strategies
with the help of trade credits, therefore more research on SCF in agricultural supply chains is needed.
Seventh, we found that papers in Clusters one, two and three predominantly adopt a modelling method, whereas Cluster four shows
some early signs of adopting an empirical method, including the mixed
method, e.g., case studies and surveys in addition to the modelling
method. More studies using empirical and case study methods are
called for in the future to gain a better understanding of SCF.
Specifically, it would be meaningful to conduct case studies to study the
business models and determinants of SCF. In future research, different
types of SCF business models could be identified. The empirical method
of statistical analysis based on secondary data is a common method
found in the empirical studies in our sample (see the supplementary
table for details). For example, to understand how bankruptcy risk is
transformed through the supply chain, Houston et al. (2016), in Cluster
four, collect and construct data on bankrupt companies and their suppliers from various sources such as BankruptcyData.com, the Moody's
Default and Recovery Database and the DealScan database. Fabbri and
Klapper (2016), in Cluster four, use firm-level data on 2500 Chinese
firms that were collected as part of the World Bank Enterprise Surveys
conducted by the World Bank with partners in 76 developed and developing countries. With the development of data science, databases
will be improved and integrated. We may have more data on different
firms or sectors, e.g., the Cosmos database provided by Teikoku Databank, which contains financial reports and firm-attribute data that include each firm's top 5 suppliers and customers (Kutsuna et al., 2016,
Cluster four). Therefore, future research could adopt a statistical analysis based on secondary data.
Supply Chain Finance (SCF) has been established as an important
but niche research area in supply chain management. The increasing
number of publications in this area supports this argument. We have
used bibliometric and network analysis tools to analyse the SCF literature, examine the evolution of this research area and identify four
research themes/clusters. In addition, based on the co-citation/clustering analysis, we carry out a content analysis focusing on AJG 3, 4 and
4* journal publications and obtain additional insights into SCF.
Adopting different objective measures, our study makes the following contributions that can help SCF researchers position their research against this mapping. First, we have mapped out the knowledge
structure of SCF and identified the contributions of journals and papers
in the SCF research. Second, we have identified existing research themes
in different streams of research (four clusters) and additional insights
from the content analysis. These themes can help SCF researchers
identify and avoid mature, saturated or stagnant research themes in
their own research. Third, seven actionable future research directions
are advanced based on the bibliometric and content analysis and topics
outside the SCF research (e.g., sustainability and tax) for SCF scholars.
Fourth, the combination of bibliometric, network and content analysis
presents a methodological contribution to systematic literature review,
as we found that the bibliometric and network analysis and content
analysis complement each other: the former lacks topical details or
insights while the latter is often criticised for a lack of academic rigour
due to the subjective judgement of researchers in coding. By combining
the two, the weaknesses could be significantly reduced, if not removed.
This paper is not exempt from limitations. We choose our keywords
according to the definition of SCF and the related literature. Although
we have tried our best, the keywords used may not be exhaustive. Some
papers that fit our inclusion and exclusion criteria were omitted because they do not have both supply chain and finance-related keywords
in the keyword section. In addition, a different set of search terms could
generate different clusters, resulting in different interpretations for the
state of art of the field. Expanding the keywords to include supplier
finance, financial supply chain, working capital optimisation, hedging,
VMI, and so on could result in a more exhaustive review of the field,
and include much border scope of papers studying the integration of
finance and operations of the supply chain.
For instance, we used the keyword “supplier” combined with all the
finance-related keywords defined in Section 2.1 and searched the
Scopus and EBSCO databases; 2444 papers were returned. We read
through these papers and identified 372. Among those 372 papers, 206
papers duplicate what we have in our sample of 348, and most of the
remaining 166 papers (approximately 75%) focus on traditional EOQ/
EPQ modelling of supplier or retailer (Cluster 1), while the others are
focused on decisions of credit period or other trade credit policy of a
single firm, which should be excluded. Furthermore, of the 166 papers,
only 34 papers are published in journals such as AJG 3, 4 and 4*.
SCF is an emerging area that has developed rapidly in recent years,
and the choice of relevant search terms is open to debate. In addition,
the constraint put on selecting papers from AJG 3 and above journals
for the content analysis also excluded certain papers that fit the inclusion criteria. Different criteria and ranking systems may also influence
the results.
Acknowledgements
We appreciate the financial support from the National Natural
Science Foundation of China (Grant No.: 71472049, 71531005,
1629001), the Ministry of Education of Humanities and Social Sciences
project (Grant No.: 17YJA630034), the Natural Science Foundation of
Fujian Province of China (Grant No. 2017J01519) and innovation team
project of the humanities and social sciences of Fudan University.
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X. Xu et al.
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