704
Technological Challenges in E-Collaboration and E-Business
ness processes poses enormous opportunity
for value creation in the supply chain and
enhances SCM practices (Horvath, 2001).
PROCESS AND SYSTEM
ALIGNMENT AND INTEGRATION:
ISSUES AND OPTIONS
Integration refers to collaborative planning
and control, decision integration, information
integration, and business process integration
EHWZHHQ LQWHU¿UP SDUWQHUV XVLQJ LQIRUPDWLRQ
technologies and systems. The technological
side of the integration is crucial to e-collabora-
tion. For example, the complexity of integration
required by e-marketplaces is one of the big
problems that have been attributed to the sharp
decline in the number of e-marketplaces. Today,
most companies have implemented enterprise
resource planning (ERP) systems to automate
their back-end planning and scheduling processes
and to undertake internal IT integration to meet
the needs of multiple vendors and customers for
years. B2B software has allowed IT integration
across companies with different IT platforms. But
there is limited application of Web technologies
to the rest of the procurement process. There is
generally a lack of real-time supply and demand
L QIR U PD W L R Q À RZ V D P R Q J V W V X S SO\ F K D L Q S D U W QH U V  
which results in inaccurate planning leading to
either inventory shortages or excessive invento-

ries. Therefore, system integration and alignment
becomes paramount to e-collaboration, and thus
affects directly bottom line results.
E-business provides organizations with oppor-
tunities to align their processes for e-collabora-
tion to attain success. However, technology-wise,
interoperability requires enhancement of existing
V\VWHPVWRWUDQVIHUWKHPLQWRDFURVV¿UPPRGH
Electronic supply chain requires integration of
software platforms or open systems across the
entire network. Integrating processes and systems
is paramount to a seamless link with partnering
companies. For example, successful implementa-
tion of electronic data exchange (EDI) requires
realignment of work processes and systems within
the network of e-collaboration. However, accord-
ing to Lowson and Burgess’ (2003) study, many
organizations, particularly small to medium-sized
enterprises (SMEs), have not taken on, or have a
limited use of, EDI and other interorganizational
systems (IOS) to integrate their supplier pro-
cesses, operations processes, and sales processes,
because they are often not able to undertake the
cost of technologies and the management systems
integration.
E-collaboration often requires the reengineer-
ing of business processes across companies, which
is very expensive in terms of time, capital, and
human resources. As one former supply chain
executive explained, it took major collaboration

efforts and 12-18 months to implement business
process reengineering between just two trading
partners (Davis & Spekman, 2004). In addition,
system integration and alignment should take
into account the diversity of e-partners. There is
KDUGO\DRQHVL]H¿WVDOOVROXWLRQIRUDOOSDUW QHUV
Take Sun Microsystems, for example. Sun has
employed three main Web technologies in its e-
network: connected ERP systems, B2B e-market-
places, and Webstores. The company enables its
large partners to directly place their orders in its
ERP systems. Other partners have the options to
choose the e-business application that suits them
EHVW7KHÀH[LELOLW\RIHEXVLQHVVDSSOLFDWLRQVWKDW
Sun provide facilitates system integration and
alignment in an optimal way. In a recent study,
de Man and der Zee (2002) suggested that there
were a number of technological lessons learned
in the process of starting and building Web ap-
plications in e-collaboration. These included that
systems should never be forced upon partners,
DQGWKDWFKDQQHOFRQÀLFWVVKRXOGEHDYRLGHGE\
selecting the right e-business application for each
partner and client group. Internal systems should
be changed to cater for the requirements of e-
QHWZRUNDQG¿QDOO\WKHRYHUDOOSURFHVVVKRXOG
705
Technological Challenges in E-Collaboration and E-Business
be guided by the concepts of standardization,
KDUPRQL]DWLRQDQGVLPSOL¿FDWLRQ

Establishing e-business process standards is
DQRWKHULVVXHIRULQWHU¿UP%%LQWHJUDWLRQZKRVH
objective is to meet the needs of global supply
chains. RosettaNet (2004) has been successful in
providing a common language for B2B transac-
tions and in building integrative e-business pro-
cesses among partners within the global trading
network. RosettaNet standards that have been used
E\)RUWXQHFRPSDQLHVZRUOGZLGH³SUHVFULEHKRZ
networked applications interoperate to execute
collaborative business process.”
There are numerous companies that specialize
in providing business-to-business integration,
synchronization, and collaboration solutions.
Global eXchange Services (GXS) is one of lead-
HUVLQWKH¿HOG*;6KDVGHVLJQHGDVHWRIVROX-
WLRQVFDOOHGWKH³([WHQGHG9DOXH&KDLQ´WRKHOS
streamline cross-enterprise business process.
The Extended Value Chain consists of four key
layers: transaction, monitoring, synchronization,
and collaboration, enabling companies to:
• Transact information with their trading
partners by enabling the transmission of
information regardless of protocol (e.g.,
TCP/IP, EDI, XML, etc.)
• Monitor their operations by providing vis
-
ibility and analytics into the movement of
information between enterprise
• Synchronize business processes by enabling

their integration, automation and optimiza-
tion
• Collaborate using solutions that leverage
cross-enterprise business processes in real
WLPH*UHHQ¿HOG
Cisco System is often cited as a successful example
of seamless integration throughout its supply chain
operating systems with its partners (Davis &
Spekman, 2004). The integration consists of three
parts: (1) planning, control, and design integra-
tion, (2) information integration, and (3) business
process integration. Planning, control, and design
integration mainly concerns making collabora-
tive decisions regarding inventory replenishment,
and collaborative product development. As the
name suggests, information integration refers
to the sharing of forecast data, inventory data,
customer order, and status information, but it
also includes system application integration with
trading partners. Business process integration
involves allowing partners to access ERP sys-
tem and MRP processes, automation of routing
of EDI data to supplier partners, automation of
FURVV¿UPEXVLQHVVSURFHVVHVDQGUHDOWLPHÀRZ
of customer orders to all partners.
FUTURE TRENDS
As e-commerce and e-business practices will
continue to grow, e-collaboration will be more
mature (rather than experimental) in nature, in
terms of the scope, quality, and credibility of on-

line customer services and products. Participating
in e-collaboration will be part of every executive’s
job in the near future. In terms of supply chain
network integration, McCormack et al.’s study
shows that most industry supply chains today have
not reached the stage at which information and
system integration is in place to build a supply
chain network (McCormack, Johnson, & Walker,
2003). Full network integration—that is, all key
business processes being online and being aligned
within the network—will be the next step that
organizations need to take to gain competitive
advantage over other supply chain networks. E-
collaboration in supply chains or virtual supply
chains will become a critical part of the future
supply chain landscape. Collaboration amongst
virtual manufacturers, virtual distributors, vir-
tual retailers, and virtual service providers will
dominate the virtual supply chains. E-business
infomediaries will leverage the Internet to perform
706
Technological Challenges in E-Collaboration and E-Business
matching of products and buyers or coordinate
marketing and transaction processes in e-col-
laborations.
E-collaboration is, and will continue to be, the
key to sustained business success. An e-business
strategy will be ineffective without an integrated
e-collaboration strategy, because the ability to
leverage collaborative relationships becomes es-

sential in today’s competitive e-business world.
Consumer/purchaser power will dominate the
e-business world and propel smaller e-busi-
nesses to collaborate to provide customers with
an ever-widening array of products and services,
real-time and rich information, and speedy and
quality transactions. Moreover, e-collaboration
helps streamline the product-to-market process
through collaborative planning and design, im-
SURYH HI¿FLHQF\ IURP WKH FKDQQHO QHWZRUN E\
reducing inventories, and ultimately generate
SUR¿WDELOLW\=KDR
CONCLUSION
(FROODERUDWLRQ UHTXLUHV LQWHU¿UP EXVLQHVV DU-
chitecture, including the reengineering of the
processes that link companies to their channel
trading partners and the development of a col-
laborative community of trading partners. It also
requires closely integrated databases and closely
V\QFKURQL]HG LQIRUPDWLRQ ÀRZV WR HOLPLQDWH
GLVWRUWLRQVDQGWKH³EXOOZKLS´HIIHFWLQWKHFRP-
munication of information between supply chain
partners. E-application architecture is imperative
WRWKHFROODERUDWLRQDQGLQYROYHV³GHWHUPLQLQJ
individual integration points between the applica-
tion and data sources, the application and back-end
installed software, and between multiple back-end
systems” (Hoque, 2001, p.153). This article has
demonstrated that information technologies have
greatly expanded the way companies do business

and partners interact with each other. The value
and the prospects that e-collaboration strategy
FDQJHQHUDWHIRUEXVLQHVVDUHFRPSHOOLQJ¿UPVWR
adopt e-collaboration technologies and systems
into their business processes. However, technol-
ogy integration and interoperability issues can be
complex. For example, data synchronization using
XML can be a formidable task in the transforma-
tion process because there are many different data
and alert types, and the published XML-based
standards do not cover all possible collaboration
data. The article highlights many implementation
issues regarding technology adoption.
REFERENCES
Damanpour, F. (2001). E-business e-commerce
evolution: Perspectives and strategy. Managerial
Finance, 27(7), 16-32.
Davis, E. W., & Spekman, R. E. (2004). The
extended enterprise: Gaining competitive advan-
tage through collaborative supply chains. Upper
Saddle River, NJ: Prentice Hall.
De Man, A. P., & der Zee, H. V. (2002). Strate-
gies for e-partnering: moving brick-and-mortar
online. Groningen: Gopher Publishers.
*UHHQ¿HOG*GXS: Enabling tomorrow’s
solutions today. Retrieved July 10, 2004, from
www.gxs.com.
Hoque, F. (2001). E-enterprise: Business models,
architecture, and components. Cambridge, MA:
Cambridge University Press.

Horvath, L. (2001). Collaboration: The key to value
creation in supply chain management. Supply
Chain Management: An International Journal,
6(5), 205-207.
Interoperability best practices: The ongoing
problems of sharing engineering data. (2004).
Strategic Direction, 20(5), 31-33.
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Technological Challenges in E-Collaboration and E-Business
Kersten, W., Schroeder, A. K., & Schulte-Bisping,
A. (2004). Internet-supported sourcing of complex
material. Business Process Management Journal,
10(1), 101-114.
Lee, H. L., & Whang, S. (2002). Supply chain
integration over the Internet. In J. Genunes et
al. (Eds.), Supply chain management: Models,
applications, and research directions (pp. 3-18).
Bordrecht: Kluwer Academic Publishers.
Lowson, R. H., & Burgess, N. J. (2003). The build-
ing blocks of an operation strategy of e-business.
The TQM Magazine, 15(3), 152-163.
McCormack, K. P., Johnson, W. C., & Walker,
W. (2003). Supply chain networks and business
process orientation: Advanced strategies and best
practices. New York: St. Lucie Press.
Neef, D. (2001). E-procurement: From strategy
to implementation. Upper Saddle River, NJ:
Prentice-Hall.
Prawel, D. (2003). Interoperability best practices:
Advice from the real world. Paper presented at

the TCT 2003 Conference, NEC, UK.
RosettaNet. (2004). Dynamic trading networks.
2SHUDWLRQDOHI¿FLHQF\1HZEXVLQHVVRSSRUWX-
nities. Investment protection (p. 6). California:
The Author.
Ross, D. F. (2003). Introduction to e-supply chain
management: Engaging technology to build mar-
ket-wining business partnerships. Boca Raton,
FL: St. Lucie Press.
Zhao, F. (2004), E-partnerships and virtual or-
ganizations: Issues and options. In M. Singh &
D. Waddell (Eds.), E-business: Innovation and
change management (pp.105-119). Hershey, PA:
Idea Group Publishing.
Zhao, F. (2006). Maximize business profits
through e-partnerships, Hershey, PA: Idea Group
Publishing.
KEY TERMS
E-Collaboration: E-collaboration refers
to the use of the Internet and/or Internet-
based tools among business partners beyond
market transactions. The term is often used
in the context of supply chain, in particular,
in supply-buyer relationships.
E-Partnership: Theoretically, e-partnership
refers to a business partnership relying on elec-
tronic (information) technologies to communicate
and interact amongst partners. As e-business
has become an integral part of most business
practices where consumers, suppliers, buyers are

connected by information technologies, the term
e-partnership is mostly associated with electronic
commerce partnerships, and in a broader sense,
electronic business partnerships.
E-SCM (E-Supply Chain Management):
E-SCM as the latest advance of SCM has two
pillars: the emerging strategic capabilities of
SCM and the Web technologies that empower
SCM. E-SCM aims to foster agile organizations
and supplier-buyer partnerships.
E-Supply Cchain Interoperability: The
ability to be fully compatible and capable of
being integrated with each other in e-business
supply chain.
Informediary: As the name suggests, info-
mediaries specialize in information management,
collecting and storing customer information and
FRQWUROOLQJ WKH ÀRZRIFRPPHUFH RQWKH:HE
Yahoo! is one of the most popular and powerful
infomediaries in the world.
Integration: Integration refers to collabora-
tive planning and control, decision integration,
information integration and business process
LQWHJUDWLRQ EHWZHHQ LQWHU¿UP SDUWQHUV XVLQJ
information technologies and systems.
RosettaNet Standards:
RosettaNet stan-
dards prescribe how networked applications
708
Technological Challenges in E-Collaboration and E-Business

interoperate to execute collaborative business
process. They provide a common language for
B2B transactions and assist in building integrative
e-business processes among partners. RosettaNet
standards consist of a three-level business process
DUFKLWHFWXUHIRULQWHUDFWLRQEHWZHHQLQWHU¿UP
e-partners: (i) partner interface processes, (ii)
RosettaNet dictionaries, including the Master
Dictionary which contains over 6000 common
terms and processes, and grammar that describes
how systems communicate, and (iii) RosettaNet
implementation framework (RNIF).
This work was previously published in Encyclopedia of E-Collaboration, edited by N. Kock, pp. 606-611, copyright 2008 by
Information Science Reference (an imprint of IGI Global).
709
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 3.3
Econometric Simulation for
E-Business Strategy Evaluation
Lidan Ha
Coppin State University, USA
Guisseppi Forgionne
University of Maryland, Baltimore County, USA
ABSTRACT
(IIHFWLYH DQG HI¿FLHQW e-business strategy de-
velopment is crucial to achieve a competitive
advantage in the electronic marketplace. How-
ever, e-business strategy evaluation constitutes
complex and dynamic challenges for business
management. This paper offers assistance for the

evaluation process by applying a computer simu-
lation that uses an econometric model delivered
through a decision making support system. The
major econometric simulation logic and method-
ology introduced here covers multidisciplinary
DUHDVDQGLVDPRQJWKH¿UVWDWWHPSWVWRLGHQWLI\
and establish a comprehensive, quantitative tool
to support the strategy development processes
of e-businesses. The authors hope to shed some
lights on e-business strategy research through
this paper.
INTRODUCTION
While bringing new opportunities (Ghosh, 1998;
Huizingh, 2002; Lumpkin & Dess, 2004; Udo &
Marquis, 2001/2002) to organizations, the devel-
opment of Internet and Web-based technologies
also presents challenges (Hoffman, 2000; Laudon
& Laudon, 2004; Porter, 2001). To many compa-
QLHVLWLVVWLOOQRWFOHDUKRZWREHQH¿WIURPWKH
Web and other digital technologies (Lumpkin &
Dess, 2004). Business strategy plays an increas-
ingly important role in order for companies to
achieve a competitive advantage in the Internet
age (Porter, 2001; Raisinghani & Schkade, 2001).
However, the new and dynamic nature of e-
businesses makes strategy development a tough
mission for companies. Comprehensive and ef-
fective tools are needed to deal with this problem
by supporting the strategy making processes of
e-businesses.

710
Econometric Simulation for E-Business Strategy Evaluation
E-business has attracted a lot of attention
Z K H Q O R RN L Q J D W G L I IHU H Q W ¿H OG VH J  P D QD J H P H QW
science, economics, econometrics, management,
and information science), The major related re-
search on e-business decision-making support
includes areas like strategy studies (Hambrick
& Fredrickson, 2001; Kim, Nam, & Stimpert,
2004; Li & Chang, 2004; McGrath & Heiens,
2003; Podgainy, 2001; Rayport & Sviokla,
1996; Rohm & Sultan, 2004), e-business models
(Afuah, 2004; Applegate, Austin, & McFarlan,
2003; Hayes & Finnegan, 2005; Lam & Harri-
son-Walker, 2003; Lumpkin & Dess, 2004; Scott
& Scott, 2004), business process reengineering
(Greasley, 2003; Gunasekaran & Kobu, 2002;
Hengst & De Vreede, 2004; Jang, 2003; Kim &
Ramkaran, 2004; Vuksic, Stemberger, & Jaklic,
2001), supply chain management (Laudon &
Laudon, 2004; Swaminathan & Tayur, 2003),
distributional channel management (Anderson
& Day, 1997; Chiang, Chhajed, & Hess, 2003;
Dykstra, 2001; Kumar, 2000; Rohm & Sultan,
2004), and customer relationship management
(Hellier, Geursen, Carr, & Rickard, 2003; Kohli,
Devaraj, & Mahmood, 2004; Peppard, 2000;
Wilson, Daniel, & McDonald, 2002).
With business strategy becoming increasingly
important (McGrath & Heiens, 2003), several

researchers present their work on how to establish
successful e-business strategies. However, these
studies are mostly qualitative in nature. What
they mainly provide is a high-level explanation
of major decision factors and their relationships
toward a company’s performance. The factors,
relationships, and their organizational effects
usually are not measured and evaluated quan-
titatively. Further, research in the management
¿HOG%HVDQNR'UDQRYH6KDQOH\6FKDHIHU
2004; David, 2003; Hambrick & Fredrickson,
2001; Robbins & Coulter, 1999) points out the
importance of support for top-level decision
making (vs. tactical-level decision making) in
an organization (Finlay, 1994). However, due to
the complex nature of top-level decision making,
information technology has not been employed
fully as with the other lower-level activities
(Finlay, 1994).
Different e-business models are explored to
help companies win on the Internet. However,
e-business models do not stand alone. Through e-
business models, organizations aim to implement
their strategy and to realize their organizational
goals. It is strategic objectives that determine the
appropriate e-business model(s), and e-business
models are among the ways to implement strategy
(Lam & Harrison-Walker, 2003).
Business process reengineering (BPR), ac-
cording to David (2003), mainly addresses short-

WHUPDQGEXVLQHVVIXQFWLRQVSHFL¿FLVVXHV WKDW
are involved in tactical decisions. Furthermore,
BPR is from a process perspective and aims to
improve an organization’s performance through
operational effectiveness. However, as pointed out
by Porter (1996), the effectiveness of individual
activities or functions within an organization is not
enough for a company to stay competitive; sound
strategy needs to be in place to guide, integrate,
and optimize overall organizational functions. In
other words, BPR practices also should be guided
by the overall strategy of an organization.
As put by researchers, process-oriented
1
sup-
ply chain management (SCM) involves decisions
that should be assessed based on an organization’s
strategic positioning (Chopra & Van Mieghem,
2000); distributional channel management (DCM)
activities should be in alignment with overall
organizational strategic goals (Anderson & Day,
1997); a strategic perspective is indispensable for
successful customer relationship management
(CRM) initiatives (Peppard, 2000).
The previous summarizes the e-business strat-
egy studies and studies that focus on tactical issues
(e.g., e-business models, BPR, SCM, DCM, and
CRM) in businesses. Certain quantitative tools
(e.g., simulation, econometrics, and analytical
tools) are provided by the prior work, but such

tools or methods are utilized mainly for lower-
level, not strategic-level, decision-making issues,
711
Econometric Simulation for E-Business Strategy Evaluation
and few quantitative tools or empirical methods
are available to evaluate e-business strategy.
This article presents the logic of using infor-
PDWLRQV\VWHPVRUVSHFL¿FDOO\GHFLVLRQPDNLQJ
support systems (DMSSs) to help to simulate and
evaluate strategic plans of e-businesses quantita-
tively and empirically. The core of this research
involves using the simulation methodology sug-
g es t e d b y m a n a g e m e n t s ci e n c e w it h g u i d a n c e f r o m
economic theories. Such a simulation approach
employs a previously developed econometric
model through a DMSS and can enhance the
quality and speed of strategic e-business deci-
sion-making processes in a comprehensive and
XVHUIULHQGO\ ZD\ 7KH UHVW RI WKLV DUWLFOH ¿UVW
explains the major research methodology, then
provides an example of implementing an actual
VLPXODWLRQZLWKDSURSRVHGV\VWHPDQG¿QDOO\
presents discussions and draws conclusions.
METHODOLOGY
The major methodology of this research is the
use of computer simulation in order to simulate
the actual strategy development processes of
e-businesses. Through this simulation, business
managers can evaluate and test their policies and
events and establish strategic plans that will im-

prove their overall organizational performance in
an effective and timely manner. Simulation is the
selected methodology because of the unstructured
and dynamic nature of e-business strategies.
According to Obaidat and Papadimitriou
VLPXODWLRQLV³WKHLPLWDWLRQRIWKHRSHUD-
tion of real-world systems or processes over time.
It is the process of experimenting with a model of
the system under study and it measures a model
of the system rather than the system itself” (p. 1).
As pointed out by Forgionne (1999), simulation
LVJRRGIRUFRPSOH[SUREOHPVDQG³>V@LPXODWLRQ
also is relatively easy to understand, offers a con-
trolled experiment, compresses time, and serves
as a mode for training decision makers” (p. 856).
The development of computer and communication
WHFKQRORJLHVKDVIXUWKHUHQKDQFHGWKHHI¿FLHQF\
of using simulations (Obaidat & Papadimitriou,
2003). In the simulation process, there are mainly
six steps (Naylor & Vernon, 1969): problem for-
mulation, model establishment, computer program
development, validation, experimentation, and
data analysis.
Problem Formulation
The problem in this research is to establish an
effective strategy for an e-business. Through
simulation, the mechanism behind the strategy
development processes can be represented in a
model, and then the model can be used to help to
evaluate and test different strategies and scenarios

and their potential impacts on an organization,
ZKLFKZLOOOHDGWRPRUHHIIHFWLYHHI¿FLHQWDQG
rational strategies without actually implementing
all those alternatives.
Model Establishment
Simulation, in general, is an experimental device.
$PRGHOZKLFKLV³DVLPSOL¿HGUHSUHVHQWDWLRQ
or abstraction of reality” (Turban & Aronson,
1998, p. 38) is the vehicle for simulation (Pidd,
1998). In practice, according to Forgionne (1999),
different types of models (e.g., physical models,
analog models, and quantitative models) can be
XVHGIRUVLPXODWLRQSXUSRVHVDQG³>L@QLWVLQLWLDO
IRUP WKH PRGHOLGHQWL¿HVWKH NH\ HOHPHQWV RI
the problem and their interrelationships” (p. 859).
The simulation outcome depends on the inputs
DQGWKHZD\WKHLQSXWVDUHUHODWHGDQGVSHFL¿HG
through a model.
1D\ORUSXUSRUWVWKDW³>Z@LWKFRPSXWHU
simulation, there are virtually no limitations
placed on the type of model structure that may be
XWLOL]HG´DQGKHXVHV³VLPXOWDQHRXVVWRFKDVWLF
difference equations” to illustrate the notion of
simulation (p. 18). As pointed out by Pritsker
(1998), equations can be used to construct simu-
712
Econometric Simulation for E-Business Strategy Evaluation
lation models. Econometric models, as a type of
quantitative model and represented by one equa-
tion or a set of equations, can identify the major

problem elements and their relationships empiri-
cally and, thus, can represent the mechanisms of
HEXVLQHVVVWUDWHJLHV6XFKDZD\RI³>P@RGHOLQJ
the dynamics of very large business or economic
V\VWHPV´ LV FODVVL¿HG DV V\VWHP VLPXODWLRQ E\
Forgionne (1999, p. 858), and econometric mod-
els are among the simulation methods that can
provide what-if analysis. For example, in a book
about corporate simulation models, Ogunsola
(1979) gives examples of econometric equations
established for an oil company.
In this article, the authors use an econometric
model that contains a system of simultaneous
equations (equations (1)-(10)). The econometric
model (equations (1)-(10)) was established in the
authors’ prior related papers (Ha, Forgionne &
Wang, 2003a, 2003b) and the dissertation
2
of one
of the authors. Usually, simulation models are
differentiated as discrete-event and continuous
models (Obaidat & Papadimitriou, 2003). If based
on the time dimension, an econometric model can
EHFODVVL¿HGDVDFRQWLQXRXVPRGHOKRZHYHULI
based on product purchases, an econometric model
can be treated as a discrete-event model. In this
current and the prior related research, we do not
consider the time effect in the model. Rather,
we focus on product purchases for a particular
time period; thus, we are doing discrete-event

simulation.
The authors’ previous papers (Ha et al., 2003a,
2003b) and research
2
establish the econometric
model representing an e-business strategy for the
problem formulated here based on general busi-
ness economics through a deductive approach.
There are reasons for using an economic-based
approach. For one thing, e-business also follows
certain basic business principles that have been
existing for decades (Besanko et al., 2004). In
addition, whether companies should set up their
e-business strategy differently than the traditional
business strategy has not been tested empirically
DQGH[SOLFLWO\LQWKHUHODWHG¿HOGV0RUHRYHUWR
follow a deductive approach, it requires that a gen-
eral theory should be in place to derive a model of
e-business strategy. Among the available theories
(e.g., cognitive, management science, accounting,
econometrics, and marketing), economic theory
is at a high level and is more comprehensive.
Finally, the basic economic motivation of e-busi-
nesses also makes an economic-based approach
more desirable.
While the model is built on general business
economics, it is operationalized from an e-business
S H U V S H FW L Y H 0 R U H V S H F L ¿FD O O \ W K H R S H U D W LRQ D O L ]H G 
model incorporates measures that are unique to
e-businesses. An e-business strategy model and

a general business strategy model mainly differ
by the different meanings or operationalizations
of the model variables.
Using econometrics enabled the authors to
achieve the following capabilities: (1) simulate
and evaluate e-business strategies empirically,
explicitly, and precisely; (2) incorporate uncer-
tainty (Palmer & Wiseman, 1999) into the analysis
through the stochastic disturbance terms in the
model’s equations; and (3) explicitly account for
the likely interrelationships among the variables in
WKHPRGHOLHUHVROYHWKHLGHQWL¿FDWLRQSUREOHP
for the model’s parameters).
The econometric model from the authors’
previous studies (Ha et al., 2003a, 2003b)
2
is re-
produced in the simultaneous equations (1)-(10) to
make the illustration in this article complete. This
model containing variables and their relationships
is established on theories with certain assump-
tions or adaptations and represents the mechanism
behind the e-business strategy formulation.
For example, equations (1)-(3) are developed
directly from economic theories (Hyman, 1986;
Landsburg, 1999; Mankiw, 2001). They specify the
GHWHUPLQDWLRQRIWKHSUR¿WUHYHQXHDQGTXDQWLW\
sold of an organization. The cost equations (8) and
(9) are a direct adoption from economic (Hyman,
0DQNLZ0DQV¿HOG<RKH

Stiglitz, 1997) and accounting theories (Weygandt,
713
Econometric Simulation for E-Business Strategy Evaluation
Kieso, & Kell, 1996). The rest of the equations
(equations (4)-(7) and (10)) identify the factors
and relate them based on a wide range of prior
work (e.g., economics, management, marketing,
and research on customer satisfaction and cus-
WRPHU OR\DOW\7KHVSHFL¿FDWLRQV DUH DFKLHYHG
by exploring different mathematical functional
forms presented by researchers (Goldberger, 1964;
Johnson, Johnson, & Buse, 1987), and the result-
ing functional forms can represent the desired
theoretical implications.
3UR¿W 5HYHQXH±&RVW  
Revenue = Quantity * Price (2)
Quantity = Min (QuantityDemanded, Quantity-
Supplied)
(3)
Price = a
0
+ a
1
(UnitCost) + a
2
ln(Product/Service)
+ a
3
ln(DistributionChannelManagement) +
a

4
ln(Competition) + a
5
ln (Product/Service)
* ln(DistributionChannelManagement) *
ln(Competition)
(4)
QuantityDemanded = b
0
(Price)
b1
* (Place)
b2
* (Promotion)
b3
* (Product/Service)
b4
*
(NetworkPerformance)
b5
* (Availabilityo
fOtherChannels)
b6
&XVWRPHU3UR¿OH
b7
*
(Goodwill)
b8
(5)
QuantitySupplied = c

0
(SupplyChainManageme
nt)
c1
* (Capacity)
c2
(PSOR\HH(I¿FLHQF\
c3
(6)
Competition = d
0
+ d
1
(Product/ServiceSubstitutes)
+ d
2
(Rivalry) + d
3
+ d
4
+ d
5
(Product/Servic-
eSubstitutes) * (Rivalry) * (SupplyChain-
Management) * (EntryBarriers)
(7)
Cost = FixedCost + VariableCost
(8)
VariableCost = UnitCost * QuantitySupplied
(9)

(PSOR\HH(I¿FLHQF\ e
0
+ e
1
ln(EmployeeQuali
¿FDWLRQe
2
ln(Training) + e
3
ln(Salary) +
e
4
ln(OtherIncentives)
(10)
For the detailed model development process
and explanations, please refer to the authors’
previous studies (Ha et al., 2003a, 2003b) and
one author’s dissertation.
2
The econometric model
(equations (1)-(10)) is aimed for general e-business
strategy development. Different e-business appli-
FDWLRQVPD\UHTXLUHGLIIHUHQWVSHFL¿FDWLRQVHJ
YDULDEOHGH¿QLWLRQVDQGIXQFWLRQDOIRUPVRIWKH
general model during the model operationaliza-
tion. Further empirical studies can be implemented
LQRUGHUWRGHYHORSRSHUDWLRQDOPRGHOVIRUVSHFL¿F
applications. (Ha & Forgionne, 2004)
Computer Program Development
2QFHDPRGHOLVGHYHORSHGIRUDVSHFL¿FVLWX-

ation, computer program(s) can be established to
simulate the strategy development process with
the model. For this purpose, we propose that a
decision-making support system (DMSS) can be
developed to implement the strategy simulation.
A DMSS can integrate the data gathering, model
operationalization, and strategy evaluation in an
HI ¿F L H Q W D Q G G \ QD P LFZ D \  )R U J L R Q QH     6 X F K 
capabilities are not readily and directly available
with other types of computer programs (e.g.,
spreadsheet implementation of the model).
The same deductive logic for the model devel-
opment applies here; the DMSS or the computer
program to be developed may have variations for
different applications, but the basic functions and
the structures of the program will be the same
across applications. In the following (Figure 1),
a general DMSS architecture (Forgionne, 2000)