sag10-strategic choices of inter-organizational information systems - a network perspective

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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


X. Zhao

Management Information Systems Department,

University of Arizona,

Tucson, AZ, USA


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)



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


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)


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.


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


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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