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7/27/2019 SAG10-Strategic Choices of Inter-Organizational Information Systems - A Network Perspective
1/12
Strategic choices of inter-organizational information systems:
A network perspective
Daning Hu & Sherry X. Sun & J. Leon Zhao & Xinlei Zhao
# Springer Science+Business Media, LLC 2010
Abstract As cooperation in a networked manner increases
via various inter-organizational information systems (IOISs),it is important to choose appropriate IOISs for different types
of organizations in the network environment. In this study, we
analyzed customer-supplier relationships among organiza-
tions in five industries using social network analysis (SNA)
methods and empirical data, aiming to help organizations
strategically choose appropriate IOISs. Three types of
customer-supplier networks were identified based on the
network centralization comparison rate: customer-centric,
supplier-centric and balanced networks. Based on the
empirical findings in our analysis, we then propose strategies
about how to choose appropriate IOISs for the firms in these
networks and discuss the pros and cons of the choices. To the
best of our knowledge, this is the first empirical research that
applied SNA methods to study customer-supplier networks in
the context of inter-organizational information systems.
Keywords Social network analysis . Inter-organizational
information systems . Customer-supplier networks
1 Introduction
Nowadays organizations are more and more connected
through various relationships such as strategic alliances
and customer-supplier relationships. In this networked
environment, recent research on the business value of
Information Technology (IT) has raised an important
issue on how multiple organizations leverage IT to create
and deliver business value. One of the most well known
multi-organizational information technologies is the
information system that links an organization to its
supplier, distribution channels, or customers. Such
systems, called inter-organizational information systems
(IOISs), are automated systems shared by two or more
organizations (Johnston and Michael 1988). They utilize
information or process capabilities in multi-organizations
to improve their performances or relationships. The well-
known examples of IOISs include American Hospital
Supply Corporations ASAP, United Airlines Apollo
reservations system, and American Airlines reservation
system SABRE.
However, there are considerable variations in the
patterns of inter-organizational relationships supported by
different IOISs. In general, IOISs are mainly used to
manage three categories of inter-organizational issues: 1)
inter-organizational transaction processing, 2) customer
relation management (CRM), and 3) supply chain manage-
ment (SCM). IOISs that manage inter-organizational trans-
action processing are those which process routine
transactions such as backorder and financial payment
among two or more organizations. One example is SABRE
system that is developed to mainly process routine trans-
actions between airlines and travel agents. CRM related
IOISs aim to track and manage interactions such as follow
up service and customer support between suppliers and
D. Hu (*) : S. X. Sun : J. L. Zhao
Department of Information Systems,
City University of Hong Kong,
Kowloon, Hong Konge-mail: [email protected]
S. X. Sun
e-mail: [email protected]
J. L. Zhao
e-mail: [email protected]
X. Zhao
Management Information Systems Department,
University of Arizona,
Tucson, AZ, USA
e-mail: [email protected]
Inf Syst Front
DOI 10.1007/s10796-010-9245-1
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customers. For instance, hospitals often install CRM
software provided by suppliers on their systems to keep
inventory stocked and thereby allow more efficient
customer-supplier interactions. SCM related IOISs manage
logistics business activities among organizations such as
tracking the shipment of goods. In addition, different IOISs
may have different impacts on the participating organizations
and industries (Choudhury 1997). In this study, we do notfocus on a specific category of inter-organizational issues but
rather study IOIS from a more general perspective
customer-supplier perspectivesince all three categories of
issues involve interactions and business processes between
the customer and supplier companies.
In order t o m ake t he m ost of t he com peti ti ve
advantages of IOISs, it is important to understand how
to choose appropriate IOISs for a specific set of
organizations or an industry. In this study, we aim to
address this problem from a customer-supplier network
perspective. We adopted social network analysis (SNA)
to model and analyze a real-world customer-suppliernetwork which consists of 3,406 organizations over a
7-year period (20022008). More specifically, we analyze the
topologies of customer-supplier networks in five major
industry sectors: IT, retail, finance, services, and health care.
The results from this empirical analysis may provide
insights for researchers and practitioners to devise
effective strategies in choosing appropriate IOISs for
organizations. To the best of our knowledge, this is the
first empirical research that applied SNA methods to
study customer-supplier networks in the context of inter-
organizational information systems.
The remainder of this paper is organized as follows. In
the next section, we review IOIS typology and SNA
methods used in this study. The third section introduces
the dataset for this study. Then we present the experimental
results. After that, we discuss the implications of the results
on the strategic choices of IOISs for organizations. Finally,
we discuss our conclusions and suggest directions for future
work.
2 Related studies
2.1 A typology of IOISs
To answer the research question of how to choose
appropriate IOISs for different organizations, one needs
to know what types of IOISs are available. Based on
several previous studies on classifications of IOISs,
Choudhury (1997) proposed a typology of IOISs that
supports one of the most common inter-organization
relationshipsthe customer-supplier (i.e., buyer-seller)
relationship. We also adopted this typology in our study.
This typology includes three types of IOISs available for
organizations:
(1) Electronic Dyads: Each supplier (customer) estab-
lishes individual, bilateral transaction links (electronic
dyads) with each of a group of customers (suppliers)
for a product or service. Figure 1 (Choudhury 1997)
shows an example of an electronic dyad IOIS amongfive suppliers and customers. For instance, Supplier 1
has built individual, bilateral exchange links with
Customers 1 and 2. A well-known example of an
electronic dyad IOIS is an electronic data interchange
(EDI) system.
(2) Multilateral IOISs: A multilateral IOIS serves as an
intermediary between an organization and its exchange
partners. It allows an organization to communicate,
interact, and exchange information and resources over a
single inter-organization link.
There are two sub-types of multilateral IOISs. The
first type is often called electronic market and allowsmultiple suppliers and customers to interact over a
single IOIS (Thomas et al. 1987) (Fig. 2a (Choudhury
1997)). One well-known example is Alibaba.com,
which is the dominant online B2B electronic market
(Zhao et al. 2008) in China. The other type of IOIS is
usually developed by a customer (supplier) to facilitate
the comparison of offers from all suppliers (customers).
This type of IOIS is called a broadcast sales system
(Fig. 2b (Choudhury 1997)).
(3) Electronic Monopolies: An electronic monopoly
IOIS supports a single source relationship for a product
or a set of products. In other words, an electronicmonopoly IOIS is a special case of the electronic dyad
system that represents the only exchange link for the
Supplier 1
Supplier 2
Supplier 5
Supplier 4
Supplier 3
Customer 1
Customer 2
Customer 3
Customer 4
Customer 5
Fig. 1 Electronic dyads
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product(s). With the electronic monopoly IOISs, the
customer has a choice of suppliers, but chooses to
establish a sole source contract for the product(s).
Choudhury (1997) assumes that these three types of
IOISs should facilitate and in turn be influenced by
three major types of customer-supplier relationships
respectively: (1) a customer always purchases specific
product(s) from a single supplier (supported by
electronic monopoly IOIS); (2) a customer purchases
products from one of a group of preferred suppliers
(supported by electronic dyads); and (3) a customer
searches the entire market for each purchase, and each
time may trade with a different supplier (supported by
multilateral IOIS). In addition, corresponding to the
second and third type of the above customer-supplier
relationships, Powell et al. (1990) reviewed sociological
and economic literature on exchange and found that
transactions may occur in a stable network of exchange
partners who have close relationships, such as a strategic
alliance, or between customers and suppliers who have
impersonal and constantly changing relationships
through markets.
Based on Choudhurys typology of IOIS and
other relevant studies, our research aims to 1) study
strategic choices of industry-specific IOISs by
analyzing patterns of customer-supplier relationships
in different industries; 2) discover additional charac-
teristics of customer-supplier networks that can help
organizations choose appropriate IOISs. Our exploratory
findings contribute to the theory-building in IOIS
research and have practical implications for various
industries.
2.2 Inter-organizational networks and social network analysis
Previous studies on strategic choices of IOISs have
addressed the growing importance of network perspectives,
especially the customer-supplier relationship (Cunningham
and Tynan 1993). Barua et al. (2004) examine a companys
customer and supplier-side digitization efforts respectively.
The results suggest that most firms are lagging in their
supplier-side initiatives relative to the customer-side.
However, supplier-side digitization has a strong positive
impact on customer-side digitization, which, in turn, leadsto better financial performance (Barua et al. 2004).
Subramani (2004) analyzed supplier networks and found
that IT deployments in supply chains lead to closer
customer-supplier relationships (Subramani 2004). Bensaou
(1997) empirically examines the cumulative influence of a
number of factors in customer-supplier relations, including
exogenous factors such as characteristics of environments
and endogenous factors such as technology applications.
He concludes that such factors conceptually and empirically
capture their collective influence on cooperation, thus
influence strategic choices. However, few studies have
examined how the patterns of customer-supplier relation-ships in a networked environment affect companies
strategic choices of IOISs. In our analysis, we focus on
studying the customer-supplier networks to derive effective
strategies for IOIS selections and implementations in
organizational networks.
We adopt social network analysis (SNA) methods to
model and analyze a large inter-organization network in
which nodes are firms and links are transactions that occur
between customers and suppliers. Social network analysis
was originally developed by sociologist Jacob Moreno
(1934) to investigate the relationship between social
structures and personal psychological well-being. He also
invented the sociograma diagram of nodes and links used
to represent relationships among social actors. In the early
development of SNA, various other ad hoc studies in
sociology, anthropology and psychology adopted similar
concepts and methods. Linton Freeman in his book (2004)
about the development of SNA observed that a growing
number of researchers contributed to SNA in the 1960s.
One of the most important research groups at that time,
Harrison White and his students at Harvard University,
Supplier 1 Customer 1
Electronic
Market
Supplier 2
Supplier 3
Supplier 4
Customer 2
Customer 3
Customer 4
Supplier(Customer)
Customer 1(Supplier 1)
Customer 2(Supplier 2)
Customer 3(Supplier 3)
Broadcasting
SalesSystem
a
b
Fig. 2 a. Electronic market, b. broadcast sales systems
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Stanley Milgram (six degree of separation) (Milgram 1967),
Wellman et al. (1996), elaborated and popularized SNA.
Nowadays, with the advance of computing technologies
and the availability of massive online data, network
analysis methods have been widely used to study large-
scale organizational networks. At the individual level,
A rakj i and Lang (2007) examined the impacts of
producer-consumer collaboration relationships on innova-tions at firms in the digital entertainment industry. Their
results can be used to devise effective firm strategies for
supporting product innovations. Buckner and Cruickshank
(2008) applied SNA methods to study the relationships
among researchers in network startups to support their
research operations. Keith et al. (2008) examined how
technology influenced the social network structure within a
group. They concluded that individuals who are proficient
with technologies tend to be more central in their group
advice networks.
At the firm level, Zack (2000) argued the importance of
using social network analysis for framing and describingthe effects of organizational systems on organizational
forms and structures. However, he did not provide any
formal framework for using SNA to study organizational
systems. Beckman et al. (2004) examined factors that affect
the firms choices of network partners. They analyzed data
on alliance networks for the 300 largest U.S. firms from
1988 to 1993. The results showed that the stability of a
firms alliance network structure depends on the type of
uncertainty it experienced. The greater the uncertainty that
a firm faces alone, the more likely this firm will expand its
alliance network. Likewise, the greater the uncertainty that
a firms market or industry faces, the more likely that firm
will strengthen the ties it presently has with others. Another
research conducted by Powell et al. (2005) studied the
determinants of the partner selection process for biotech-
nology firms in the 1990s. Several types of determinants
such as preferential attachment and homophily (i.e., people
tend to interact with others having similar characteristics)
were statistically examined using McFaddens (1980),
McFadden and Zarembka (1974) discrete choice model.
Carley (2002) developed a framework for computational
analysis of social and organizational systems (CASOS)
which utilized various computational approaches including
agent technology and dynamic social network analysis.
However, little research has been done on the customer-
supplier relationships using SNA methods.
2.2.1 Social network analysis methods
In this section, we review several SNA measures we used in
this study to analyze customer-supplier networks across
different industries. At the individual node level, centrality
measures are used to identify key members and interaction
patterns between sub-groups. One of the most commonly
used centrality measuresa nodes degreeis defined by
Freeman (1979) as the number of direct links this node has.
It measures how active a particular node is. A network
member with a high degree can be the leader or hub in a
network.
In addition, several network-level SNA measures such as
average degree, clustering coefficient, average path length,and degree distribution are used to describe and distinguish
different network topology models. Three models have
been employed to characterize complex networks: random
graph model, small-world model (Watts and Strogatz 1998),
and scale-free model (Barabasi and Alert 1999). In random
networks, most of the nodes have roughly the same number
of links.
Clustering coefficient is usually used to determine the
small-world nature of social networks. It is the probability
that two nodes with a common neighbor also link to each
other. A small-world network usually has a significantly
larger clustering coefficient (Watts and Strogatz 1998) thanits random model counterpart, indicating a high tendency
for nodes to form clusters and communities. A small-world
network also usually has a relatively small average path
length (i.e., average number of steps along the shortest
paths for all possible pairs of network nodes) (Watts and
Strogatz 1998).
Degree distribution P(k) is the probability distribution
that a node has exactly k links. Power-law degree
distribution is used to characterize scale-free networks
(Wasserman and Faust 1994). In such networks, a small
fraction of the nodes have a large number of links while a
big fraction of nodes have just a few. This scale-free
topology may be caused by the newly joined nodes
preferential attachment to the nodes with high degrees
(Laender et al. 2000).
3 Data and methods
In this study, the customer-supplier transaction data for
major U.S. firms is extracted from the Standard & Poors
COMPUSTAT database. COMPUSTAT is a database which
provides financial, statistical and market information on
companies around the world to institutional investors,
bankers, advisors, and analysts in corporate, private equity,
and fixed income markets. This database covers more than
88,000 global securities, covering 98% of the worlds
market capitalization, and provides nearly 40 years of
company data history.
According to the FASB (Financial Accounting Standard
Board) regulation No. 14, firms need to report certain
financial information for any industry segment that com-
prised more than 10% of consolidated yearly sales, assets,
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or profits. The COMPUSTAT dataset used in our study
retrieved companies customer-supplier relationships from
such reports. It also includes information such as sales,
reporting year, and other related disclosures. The whole
customer-supplier dataset in the COMPUSTAT database
ranges over a 7-year period from 2002 to 2008.
Figure 3 shows a sample set of data extracted from the
COMPUSTAT customer-supplier dataset. This sample
includes five suppliers and three customers. Other information
includes total sales between the customer and supplier,
reporting year, and their NAICS codes. A row in this data
sample indicates a record of the customer-supplier relation-ship between these two firms. For example, as the second row
shows, in 2004 IBM purchased $96.4 million worth of data
storage products from ADI Corporation.
Table 1 summarizes the COMPUSTAT customer-supplier
dataset used in our study. This dataset includes transaction
information involving 5,636 supplier firms and 13,065
customer firms from 2002 to 2008.
From the COMPUSTAT customer-supplier dataset, we
focused sub-datasets in five major industries: Information
Technology (IT), retail, service, finance, and health care.
The IT industry is included in our analysis to see if IT
companies have special patterns in selecting IOISs sinceIOISs are essentially an IT product. The retail industry is
selected since nowadays retail companies heavily rely on
information systems to manage their relationship with
suppliers in transaction management and supply chain
management (e.g., Walmart). The finance industry is selected
because most inter-organizational financial transactions today
are computer-based and depend on inter-organizational
information systems. Service and health care industries are
included because they are considered as very promising
application areas for inter-organizational information systems
(De and Ferratt1998; Bakos 1991).
The companies from these five industries are extracted
based on each firms NAICS (North American Industry
Classification System) code. Each NAICS code represents a
specific industry category. For example, Intel, the
worlds largest semiconductor chip maker, has NAICS
code 334290 which is in the category of computer and
electronic product manufacturing industry. For each
industry, we first extracted all the firms in related
NAICS categories and their trading partners (customers
or suppliers). Then we construct the network using the
extracted firms as nodes and the customer-supplier
relationships among them as links.
In this research, both the quantitative methods and
interpretive findings are needed for studying the IOIS
choices. The quantitative methods are used to analyze thetopologies of customer-supplier networks, while the inter-
pretative findings can provide the contextual interpretations
of such topologies and help researchers and practitioners
devise effective strategies for choosing appropriate inter-
organizational information systems.
4 Results
4.1 Basic statistics
We conducted our analysis using the functions provided bythe most widely used social network analysis software
Ucinet 6. Each of the measures/functions is widely used in
other social network analysis studies. Table 2 summarizes
the basic statistics of the five constructed networks. IT,
retail and service networks are relatively larger than other
networks since they include firms across multiple real-
world industries. This is mainly due to the categorizing
method NAICS used. For instance, according to NAICS,
the service network includes firms that provide all kinds of
professional, scientific, and technical services ranging from
tax preparation to interior design. On the other hand, firms
in the finance and health care networks usually only
focused on businesses within these two industries.
S_ID C_ID Supplier CustomerSales
(Milllion)Product Year S_naics C_naics
63593 5680 QEP CO HOME DEPOT 67.193 Hardware 2005 332212 444110
63644 6066 ADI CORP IBM 96.4 Data Storage 2004 334112 541519
109522 11259 MED GEN WALMART 1.315 Healthcare Products 2003 541611 452990
122394 6066 PERFICIENT IBM 12.874 Consulting Services 2006 541519 541519
178538 5680 ZEP INC HOME DEPOT 66.25 Cleaning Products 2005 325612 444110
Fig. 3 Sample data of
COMPUTSTAT customer-
supplier dataset
Table 1 Basic statistics of COMPUSTAT customer-supplier dataset
Time period Number of
suppliers
Number of
customers
Number of
products
20022008 5,636 13,065 5,035
Table 2 Key statistics of the constructed customer-supplier networks
across different industries
IT Retail Service Health care Finance
# of nodes 742 273 501 87 303
# of Links 862 279 469 78 249
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4.2 SNA centrality measures
We use SNA centrality measures to describe the topology
of the five constructed customer-supplier networks. The
customer-supplier relationship is a directional link which
indicates flows of goods or services exchanged between
supplier and customer companies. Thus we need to
measure the network for both in links and out links.The average in-degree is actually the average number of
suppliers a customer has and the average out-degree is
the average number of customers a supplier has.
Moreover, the maximum in(out)-degree is the largest
number of suppliers (customers) a firm has in the
measured network.
Table 3 shows the centrality measures of all five
constructed networks. We found that, in most of the networks,
the average in-degree is larger than the out-degree. This
indicates that in general there are more suppliers than
customers in these five networks. Particularly, the
average in-degree of the retail network is significantlylarger than its average out-degree. This may be because
retail industry firms mainly serve individuals rather than
organizations.
In addition, we measured network centralization and
maximum degree for both in and out links in those five
networks. Network centralization aims to measure the
degree to which an entire network is focused around a
few central nodes (Kang 2007). It indicates the levels of
inequality or variance in the observed network as a
percentage of that of a perfect star network of the same
size. In the customer-supplier network, if the network
centralization for in(out) links is much larger than the
network centralization for out(in) links, it means that there
is a substantial level of concentration or centralization of
links on suppliers (customers). The network positional
advantages for suppliers (customers) are rather unequally
distributed in this network.
This measure has particular importance for inter-
organizational studies of coordination and leadership. Turk
(1977) argues that inter-organizational network centraliza-
tion can be equated with coordination. Irwin and Huges
(1992) define network centralization as the degree to
which an inter-organizational network is dominated by a
few places. In our study, network centralization for in
links (out links) reflects the degree to which a customer-
supplier network is dominated by supplier (customer)
companies.
In this study, since we focused on customer-supplier
(directed) networks and each node can be a customer or asupplier, one important question is which type of nodes
(customer or supplier) has dominant positional advantage in
each industry-specific network. To address this question,
we construct a new measurenetwork centralization
comparison rate which is the network centralization
for in links divided by the network centralization for out
links. If the network centralization comparison rate is much
larger than 1, it means that supplier companies have much
higher positional advantages over customer companies.
In the context of our study, we defined the supplier
(customer) companies having dominant positional advantages
in a customer-supplier network as this network has/with anetwork centralization for in(out) links that is 2 times larger
than (its) network centralization for out(in) links.. Thus the
five networks are categorized into three typescustomer-
centric networks, supplier-centric networks and balanced
networksusing the network centralization comparison rate
. If 0 < a 0:5, the network is a customer-centric network
(i.e., customer nodes have dominant positional advantage). If
2, the network is a supplier-centric network. Networks
with 0:5 < a< 2 are defined as balanced networks since
neither type of the companies (nodes) has dominant
positional advantage over each other.
The comparison rates for all five customer-supplier
networks are presented in the last row of Table 3. It was
found that network centralization comparison rates for the
in links of IT, retail and service networks are larger than 2.
This means, in those networks, several firms that have
many suppliers take the central positions while the
remaining firms are peripheral. These networks are
customer-centric networks. On the other hand, the network
centralization comparison rate of the health care network is
0.49, indicating supplier firms take central positions. Thus
Table 3 Centrality measures of customer-supplier networks
IT Retail Service Health care Finance
Average in-degree (suppliers) 2.3 3.40 2.11 1.93 1.69
Average out-degree (customers) 2.2 1.42 1.85 1.93 1.56
Maximum in-degree (suppliers) 44 58 77 5 11
Maximum out-degree (customers) 13 14 11 9 9
Network centralization for in links (%) 5.82 21.0 15.24 4.81 3.42
Network centralization for out links (%) 1.63 4.84 2.02 9.65 2.73
Network Centralization Comparison Rate (in links/out links) 3.57 4.34 7.54 0.49 1.25
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the health care network is a supplier-centric network. In
addition, the finance network has a network centralization
comparison rate of 1.25, which indicates it is a balanced
network with customer and supplier companies having
similar positional advantages.
4.3 SNA visualization analysis
We used the network visualization tool NetDraw to visualize
the topology of all five networks. The observations from
these visualizations also provide evidence to support our
findings about customer-/supplier-centric networks and
balanced networks.
In both Fig. 4a and b, the large blue nodes represent
major customer firms while the yellow nodes represent
major supplier firms. It is obvious that customer firms
dominate in the sample IT network (Fig. 4a) and take most
central positions (customer-centric), while supplier firmshave more positional advantages in the sample health care
network (Fig. 4b) (supplier-centric).
Fig. 4 a. Sample IT customer-supplier network (customer-centric), b. Sample health care customer-supplier network (supplier-centric)
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As Fig. 5 shows, the finance network has similarnumbers of customer and supplier firms with a large
number of exchange links. This is also consistent with its
centrality measures. The average degrees, maximum
degrees and network centralization measures for both in
and out links in the finance network do not differ much.
Thus, neither customer firms nor supplier firms have
positional advantage in the finance network. We defined
such networks as balanced networks.
4.4 Component analysis
Component analysis mainly aims to investigate the con-nectivity of the companies in each industry through the
supply relationship. Components of a network are sub-
networks that are connected within, but disconnected
between, sub-networks. The proportion of the largest
component to the whole network indicates if there is a
core set of companies that are closely inter-connected with
each other through the supply relationship in each industry.
Such core companies often have advantages in gaining and
efficiently distributing information, knowledge and resour-
ces because they are closely connected with other core
companies. In contrast, fragmentation rate shows the
proportion of the network that cannot reach each other. In
addition, the larger the fragmentation rate the larger theproportion of companies that cannot reach each other. Thus
large fragmentation rate often indicates that the network has
a lot of disconnected nodes or components. These two
measures illustrate the overall connectivity of the customer-
supplier network in an industry.
The rationale for component analysis is to derive
empirical propositions about IOISs from the results. For
instance, networks with large core components have better
capabilities in distributing information and resources. From
the IOIS perspective, these networks are more suitable for
broadcast sales systems since such systems often require
good network connectivity to broadcast information to asmany companies as possible.
In this research, component analysis was conducted on
all five customer-supplier networks. The proportion of the
largest component to the network and the fragmentation
rate are reported in Table 4 for each of the five networks.
We found that the proportions of the largest clusters in the
three customer-centric networksIT, retail and service
are 65%, 60.9% and 71.6% respectively, which accounts for
more than 60% of their corresponding networks. On the
other hand, the largest clusters in the health care and
finance networks only include 32.1% and 35.7%, around
30% of the total number of nodes those networks have. In
Fig. 5 Sample finance
customer-supplier network
(balanced networks)
IT Retail Service Health care Finance
Number of nodes 742 273 501 87 303
Size of the largest component (nodes) 560 170 336 25 89
Proportion of the network 65% 60.9% 71.6% 32.1% 35.7%
Fragmentation rate 43% 60.1% 54.9% 86.5% 87.8%
Table 4 Results of the compo-
nent analysis
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addition, the fragmentation rates of the health care network
and finance network are larger than the three customer-
centric networksIT, service and retail networks. These
results from the component analysis indicate that these
three customer-centric networks are often much more
connected than the supplier-centric networks. This implies
that broadcast sales systems are suitable for customer-
centric networks.
4.5 Topological analysis
SNA topology measures are developed to describe the
topologies of the constructed customer-supplier networks.
Table 5 shows the results of these measures on the five
constructed networks.
The results of topological analysis show that the
average clustering coefficients for all networks are quite
small, indicating that the firms in these networks have
little tendency to cluster. This shows that the inter-
organizational customer-supplier networks are different
from most social networks of individuals which usually
tend to cluster together to form small-world networks.
One possible explanation is that organizations may have
more obstacles to overcome to form closely connectedclusters, such as legal issues, competition within the
industry, or organizational culture differences.
The results also show that the average path lengths of the
three customer-centric networks are much larger than the
other two. This is consistent with the cluster analysis
results. Since the health care and finance networks have
many disconnected small clusters with short path length,
the average path length of these two networks is much
smaller than the customer-centric networks.
Global efficiency is the average of the inverses of the
lengths of the shortest paths over all pairs of nodes in a
network. It is usually used to measure the communicationefficiency of the network. In the customer-supplier networks,
it measures how efficient the networks are in terms of
transferring/exchanging information and products. The results
show that customer-centric networks are more efficient than
supplier-centric networks.
Table 6 shows the results of linear regression of the
degree distributions for all five networks. It was found
that all networks follow a power-law degree distribution
(Newman 2001), modeled by p(k)k, while k is the
degree and p(k) is the probability a node has degree k in
the network under study. Most coefficients of determina-
tion R2 for the regressions are larger than 0.9 (ranging
from 0 to 1), indicating high fitness of the power-law
degree distributions. Thus all customer-supplier networks
show scale-free features. These results indicate that, for all
five networks, they more or less have the structure that a
small number of nodes have a large number of links while
the majority of nodes only have few.
5 Findings and discussion
In this section, we first summarize our findings and then
link our findings to the research question about how to
choose appropriate IOISs for organizations.
& There are three main types of customer-supplier networks:
customer-centric, supplier-centric and balanced networks.
In customer-centric networks, a small group of customer
firms that have many suppliers take the central positions
while the remaining firms are peripheral. Such large
customer firms have more network positional advantages
in terms of access to resources and communication
efficiency than other firms in the networks. Supplier-
centric networks have similar features with suppliers as
the central nodes. In balanced networks, neither customer
nor supplier firms have positional advantage.
& Customer-centric networks are usually larger since they
include firms which have business operations acrossmultiple industries, while supplier-centric networks
only include firms that focus on business within one
industry.
IT Retail Service Health care Finance
Average Clustering Coefficient 0.005 0.001 0.001 0.001 0.003
Average Path Length 3.34 1.64 2.803 0.414 0.878
Global Efficiency 0.112 0.117 0.091 0.059 0.028
Table 5 Results of the topolog-
ical analysis
IT Retail Service Health care Finance
Goodness of Fit R2 0.92 0.88 0.97 0.78 0.91
Power-Law Distribution Exponent 2.21 2.04 2.22 1.71 2.32
Table 6 Results of linear
regressions on degree distribu-
tions of customer-supplier
networks
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& Among the five networks constructed based on
NAICS industry code, IT, retail and service are
customer-centric networks; the health care network
is a supplier-centric network. In the finance network,
neither customer firms nor supplier firms have
dominant positional advantages. Thus the finance
network may represent a network form in which
customers and suppliers are in balance in terms of
network position and embeddedness.
& SNA cluster analysis showed that customer-centricnetworks are much more connected than other net-
works, indicating they may have more critical
relationships which bridge small local clusters to the
rest of the network.
& SNA topological analysis showed that customer-
centric networks are more efficient than supplier-
centric networks in terms of exchanging information
and resources.
& It was also found that all five networks show strong
scale-free features. In these customer-supplier networks,
a small group of nodes has a large number of links
while the rest only have a few links.
Our research objective is to utilize the above findings
to develop empirically-driven propositions for choosing
appropriate IOISs for firms in different types of customer-
supplier networks. In Table 7, we link the above findings
with the three major types of IOISs proposed by Choudhury
(1997). It was suggested that 1) broadcast sales systems are
suitable for firms in customer-centric networks; 2) broadcast
sales systems or electronic dyad systems are suitable for
supplier-centric networks; and 3) electronic market systems
are suitable for balanced networks.
The reasons for these suggestions are summarized as
follows. 1) We suggest broadcast sales systems for
customer-centric networks because customer firms are
central in such networks and have many positional
advantages in accessing product information from suppliers
and broadcasting the requirements. 2) For similar reasons,
we also recommend broadcast sales systems (supplier firm
as users) to firms in supplier-centric networks. However, in
some industries, the supplier-centric networks are less
connected, with many disconnected cliques or even pairs
of firms. It may not be efficient to use a broadcast sales
system to broadcast or collect product information. In such
disconnected cliques, electronic dyad IOISs may work
better since such systems mainly improve the local
efficiency between the customer firm and a group of its
preferred suppliers.
3) We suggest firms in balanced networks use
electronic market IOISs. In balanced networks both
customer and supplier firms are equally distributed and
it would have similar effects to broadcast productinformation from either customer or supplier firms.
Therefore, electronic market may be the most appropriate
form of IOIS since it treats all participating firms equally
and improves transaction efficiency in general.
6 Conclusions and future directions
In this paper, we aim to devise effective strategies based
on empirical findings to help firms to choose appropriate
inter-organizational information systems. We first reviewed
the typology of existing IOISs proposed by Choudhury. Then
we used social network analysis to model and analyze real-
world customer-supplier networks in five service-related
industries. Our analysis identified three types of customer-
supplier networks based on their topological properties:
customer-centric networks, supplier-centric networks, and
balanced networks. According to their distinctive char-
acteristics and functions, we then propose several
strategies in choosing IOISs for these three types of
networks. We argue that each proposed strategy may
better utilize the advantages offered by its corresponding
customer-supplier network and IOIS.
Our future work consists of several directions including
(1) extending the granularity of the classification of IOIS
and the customer-supplier networks, and thereby providing
more specific recommendations on IOIS strategies for
practitioners, (2) empirically evaluating the effectiveness
of the strategies proposed in our study, and (3) investigating
the relationships between IOIS choices and firm performance.
Our efforts will open a new venue of research in
understanding the IT value in networked enterprises by
incorporating insights from network analysis of inter-
Table 7 Proposed IOISs for firms in different types of customer-supplier networks
Networks Features IOISs
Customer-centric networks
(e.g., IT, service, retail)
Positional advantages for customer firms; more connected;
more efficient in terms of information exchange
Broadcast Sales Systems (customer
firm as users)
Supplier-centric networks
(e.g., health care)
Positional advantages for supplier firms; many disconnected
cliques; less efficient
Broadcast Sales Systems (supplier firm
as users) OR Electronic Dyad
Balanced networks (e.g.,finance)
Customers and suppliers have similar positional advantage;less connected
Electronic Market
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organization relationships and developing better strategies for
choosing appropriate IOISs.
Acknowledgements This research is supported by City University
of Hong Kong Start-up Grant (Grant Number 7200102) and Strategic
Research Grant (Grant Number 7002257).
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Dr. Daning Hu received his Ph.D. degree in Management Information
Systems from Eller College of Management, the University of
Arizona, Tucson, Arizona, USA. He is currently a research fellow at
the Department of Information Systems, City University of Hong
Kong. His research interests include business intelligence, financial
system risk management, social network analysis and open source
communities. Before joining City University of Hong Kong, he
worked as a research associate at the University of Arizona.
Dr. Sherry Sun received the M.S. and Ph.D. degrees in Management
Information Systems from Eller College of Management, the
University of Arizona, Tucson, Arizona, USA. She is currently an
assistant professor at the Department of Information Systems, City
University of Hong Kong. Her research mainly focuses on the
development of workflow technology and its applications in electronic
commerce, knowledge management, and organizational process
automation. Before joining City University of Hong Kong, she
worked as a database developer in the Artificial Intelligence Lab at
the University of Arizona and a database administrator in Arizona
Cancer Center.
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Dr. J. Leon Zhao is Head and Chair Professor in Information
Systems, City University of Hong Kong. He was Interim Head and
Eller Professor in the Department of Management Information
Systems, University of Arizona, previously. He holds Ph.D. and M.S.
degrees from the Haas School of Business, UC Berkeley, M.S. in
Engineering from UC Davis, and B.S. from Beijing Institute of
Agricultural Mechanization. Leon's research has been supported by
NSF, SAP, and other funding agencies. Leon has served as associate
editor of Information Systems Research, ACM Transactions on MIS,
IEEE Transactions on Services Computing, Decision Support Systems,
Electronic Commerce Research and Applications, among other jour-
nals. He has co-edited more than ten special issues in variousIS journals
including Decision Support Systems and Information Systems Frontiers
and has chaired numerous international conferences including the 2010
Conference on Design Science Research, the 2009 IEEE Conference on
Services Computing, the 2008 IEEE Symposium on Advanced
Management of Information for Globalized Enterprises, the 2007
China Summer Workshop on Information Management, the 2006 IEEE
Conference on Services Computing, among others. He received an IBM
Faculty Award in 2005 for his work in business process management
and services computing and was awarded Chang Jiang Scholar Chair
Professorship at Tsinghua University by the Ministry of Education of
China in 2009.
Mr. Xinlei Zhao is a Ph.D. in Management Information Systems,
Eller College of Management, the University of Arizona. His main
research interests focus on Modeling and Analysis Methodologies for
Business Processes and Workflow, and Service Computing. His
research has appeared at academic conferences including WeB,
HICSS, and AMCIS.
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