394
Dynamic Pricing for E-Commerce
places, products traded in posted-price markets
are no-niche items and exhibit continuous demand
over time. The Web site of online book merchant
Amazon () is an example
of a posted-price market. A buyer interested in a
particular book enters the necessary information
through a form on Amazon’s Web site to request
the price of the book and receives the price in
response.
Modern seller Web sites employ automated
techniques for the different stages of e-commerce.
Intermediaries called intelligent agents are used
to automate trading processes by implementing
different algorithms for selling products. For ex-
ample, Web sites such as MySimon (http://www.
mysimon.com) and PriceGrabber (http://www.
pricegrabber.com) automate the search stage
by employing the services of intelligent agents
called shopbots. Shopbots enable buyers to make
an informed purchase decision by comparing
the prices and other attributes of products from
thousands of online sellers. Automated price
comparison by buyers has resulted in increased
competition among sellers. Sellers have responded
to this challenge by using intelligent agents
called pricebots that dynamically determine the
price of a product in response to varying market
conditions and buyers’ preferences. Intelligent
agents are also used to enable other e-commerce

processes, such as supply-chain management and
automated negotiation.
In this article, we focus on the different
algorithms that sellers’ pricebots can use for
the dynamic pricing of goods in posted-price
markets.
BACKGROUND
Over the past few years, online dynamic pricing
has stimulated considerable interest in both the
commercial and research communities. Increased
SUR¿WVDQGUDSLGO\FOHDULQJLQYHQWRULHVUHVXOW-
LQJIURPHI¿FLHQWSULFLQJKDYHHQFRXUDJHGWKH
development of software pricing tools includ-
ing Azerity () and Live
Exchange (). Automated
dynamic pricing for posted-price markets has
been implemented and analyzed using simulated
market models (Brooks, Gazzale, MacKie-Mason,
& Durfee, 2003; Dasgupta & Melliar-Smith, 2003;
Kephart, Hanson, & Greenwald, 2000). Most of
these models consider the price of a product as
the only attribute affecting a buyer’s purchase
decision. Surveys of consumers who purchase
products online, reported in Brown and Gools-
bee (2000) and by ResellerRatings (http://www.
resellerratings.com), reveal that online buyers
are frequently willing to pay an elevated price
for particular product attributes such as delivery
time, seller reputation, and service. Moreover, the
preferences of buyers vary over time depending

on exogenous factors such as sales promotions,
aggressive advertising, and the time of year.
Therefore, it is important for an online seller to
differentiate a product using multiple attributes
and to determine the purchase preferences of a
potential buyer over those attributes so that the
seller can tailor its offer to the buyer’s require-
PHQWVDQGLPSURYHLWVSUR¿WV
In online markets, a seller must determine the
prices that its competitors charge for a product so
that it can place its price at a competitive advan-
WDJH7KHUDSLGÀXFWXDWLRQRIPDUNHWSULFHVFDQ
leave a seller with outdated competitor price infor-
mation that can cause the seller’s dynamic-pricing
algorithm to function incorrectly. However, it is
GLI¿FXOW IRUVHOOHUVWR REWDLQ SULRU LQIRUPDWLRQ
about buyers’ parameters. Therefore, it is desirable
if online sellers do not assume prior knowledge
about market parameters, but rather use a learn-
ing algorithm (Brooks et al., 2003; Dasgupta &
Hashimoto, 2004) to determine changing market
parameters dynamically.
395
Dynamic Pricing for E-Commerce
DYNAMIC PRICING USING
INTELLIGENT AGENTS
In an automated posted-price market, a seller em-
ploys the services of a pricebot that dynamically
FDOFXODWHVDSUR¿WPD[LPL]LQJSULFHRIDSURGXFW
LQUHVSRQVHWRÀXFWXDWLRQVLQPDUNHWSDUDPHWHUV

VXFKDVWKHSULFHVDQGSUR¿WVRIFRPSHWLQJVHOO-
ers and the reservation prices of buyers. The
seller posts the updated product price at regular
intervals to attract buyers while maintaining a
competitive edge.
The market model we consider is based on
the shopbot economy model of Kephart, Hanson,
and Greenwald (2000), which makes simplify-
ing assumptions about the online economy that
facilitate analysis while retaining the essential
features of the market. It consists of S sellers who
compete with each other for B buyers (BS).
Only one type of commodity is traded in the
PDUNHW$VHOOHUEHKDYHVDVDSUR¿WPD[LPL]HU
DQGKDVDVXI¿FLHQWVXSSO\RIWKHFRPPRGLW\IRU
the lifetimes of the buyers. Buyers return to the
market repeatedly to purchase the commodity.
Examples of such markets include telephone and
Internet services.
A product is characterized by multiple attri-
butes. A seller offers a slightly different price for
the product along each of its attributes. As shown
LQ)LJ XUHDEX\HU¿ UVWUHTXH VW VDTXRWHI URPWKH
sellers for the price based on his or her preferred
product attribute, and then selects the seller that
makes the best offer. The buyer’s preferred at-
tribute is not revealed to a seller when the buyer
PDNHVDTXRWHUHTXHVW7KHUHIRUHDSUR¿WPD[L-
mizing seller must determine a buyer’s preferred
attribute in response to the buyer’s quote request.

The seller then calculates a competitive price for
the product along the buyer’s preferred attribute
and makes an offer to the buyer.
Dynamic-Pricing Algorithms
%HFDXVHRQOLQHVHOOHUVDUHSUR¿WPD[LPL]HUVWKH
objective of a seller is to determine a price for
each attribute of the product that maximizes the
VHOOHU¶VSUR¿W+RZHYHUWKHSULFLQJIXQFWLRQRID
seller cannot be stationary as there are other com-
peting sellers who revise their prices to improve
their offers and attract buyers away from each
other. Therefore, the seller updates the prices it
charges on different product attributes at intervals
in response to competitors’ pricing strategies and
changes in the buyers’ preferred attributes.
We describe in the following sections some
pricing algorithms used by an online seller’s
pricebot to determine the price of a product. We
omit the subscript for attribute a
i
in the price and
SUR¿WQRWDWLRQIRUWKHVDNHRIFODULW\:HLOOXVWUDWH
the algorithms for a single seller assuming that it
Figure 1. A hypothetical market showing two buy-
ers, B1 and B2, with preferred attributes a1 and a3,
respectively, making a quote request to four sellers,
S1, S2 , S3, and S4, and then selecting the seller
that offers the best price for the product on their
respective attributes. The four-tuple below each
seller denotes the normalized price on the different

product attributes offered by that seller.
B
1
B
2
S
1
S
3
S
2
S
4
< 0.8, 0.4, 0.3, 0.5 >
< 0.9, 0.3, 0.6, 0.7 >
< 0.7, 0.2, 0.8, 0.1 >
< 0.6, 0.1, 0.7, 0.4 >
Preferred
attribute is a
Selects seller S
1
4
Preferred
attribute is a
Selects seller S
3
1
Get current offer from
sellers for attribute a
1

Get current offer from
sellers for attribute a
3
396
Dynamic Pricing for E-Commerce
is competing with other sellers in the market. We
use p
t
to denote the price charged by the seller
during interval t.
Derivative-Following Algorithm
In the derivative-following (DF) algorithm, a
VHOOHUXVHVLWVSUR¿WLQIRUPDWLRQVLQFHWKHODVWSULFH
update to adjust its price in the next interval. If
WKHSUR¿WLQWKHODVWLQWHUYDOKDVLQFUHDVHGIURP
its previous value, the price for the next interval
continues to move in the same direction as in the
ODVWLQWHUYDO2QWKHRWKHUKDQGLIWKHSUR¿WLQ
the last interval has decreased from its previous
value, the direction of the price movement is the
reverse of the direction in the last interval. The
equation for updating the price during interval
W1 using the DF technique is given by:
p
W1
= p
t
G
t
sign(S

t
- S
t -1
)sign(p
t
- p
t -1
)
Here, S
t
UHSUHVHQWVWKHSUR¿WPDGHE\WKHVHOOHU
during interval t, and G
t
represents the amplitude
of the price change and is drawn randomly from
the uniform distribution U[l, u], where l > 0 and
u > 0.
In the DF algorithm, the price of the product
LVXSGDWHGEDVHGRQWKHSUR¿WLQIRUPDWLRQIURP
only the last interval. Therefore, the DF strategy
LVQRWYHU\HI¿FLHQWLQG\QDPLFDOO\WUDFNLQJWKH
SULFHRIDSURGXFWLQDUDSLGO\ÀXFWXDWLQJPDUNHW
$PRUHHI¿FLHQWWHFKQLTXHWKHmodel-optimizer
(MO) algorithm described next, employs the his-
WRULFDOSULFHDQGSUR¿WLQIRUPDWLRQRIWKHVHOOHUWR
update the price during the next interval.
Model-Optimizer Algorithm
A seller using the MO algorithm maintains its
SULFHYVSUR¿WSUR¿OHRYHUWKHODVWh intervals,
where h denotes the size of the history window

of the seller, as shown in Figure 2 for h = 5. The
MO algorithm works as follows:
1. Assign weights to the last h points in the
SULFHYVSUR¿W SUR¿OH RI WKH VHOOHU 7KH
weight of a point expresses its relevance to
current market conditions. Older points are
less relevant and are assigned lower weights;
more recent points are more relevant and are
assigned higher weights.
2. Fit a polynomial over the
h points in the his-
tory window of the seller using a nonlinear
regression approach.
3. Use a nonlinear optimization scheme, like
the Nelder-Mead algorithm (Nelder &
Mead, 1965), to determine the price that
FRUUHVSRQGVWRWKHPD[LPXPSUR¿W
Although a large history window h might yield
accurate results, it increases the time required for
calculating the price for the next interval. If the
seller is relatively slow in dynamically updat-
ing its price, its competitors might outperform
it. Therefore, the value of h should be selected
carefully to balance accuracy with rapidity in
price calculation.
Figure 3 shows the variation in prices over time
for three competing sellers in a market using the
02DOJRULWKP$VVKRZ QLQWKH¿J X UHWKHVHOOHUV
using the MO algorithm engage in repeated cycles
of price wars with each other. The reason for the

price wars is that there are buyers with different
Figure 2. The operation of the MO algorithm
with h = 5
397
Dynamic Pricing for E-Commerce
preferences in the market. For simplicity, we as-
sume that there are only two types of buyers.
•
A-type buyers that do not have price as
the preferred attribute. Such buyers select
a seller using other undetermined criteria,
which we model as selection at random.
•
B - t y p e b u y e r s t h a t h a v e p r ic e a s t h e p r e f e r r e d
attribute. Such buyers shop for the lowest
price in the market and select the seller of-
fering the lowest price.
Based on a survey of online markets (Clark,
2000), we assume that the ratio between A-type
and B-type buyers in the market is 1:3. Because
B-type buyers are greater in number, they gen-
erate the majority of the revenue for the sellers.
Therefore, the sellers reduce the price of the com-
modity in successive intervals so that they can
attract the maximum number of B-type buyers
by offering the lowest price among competitors,
thereby undercutting each other. This price war
continues until each seller’s price reaches the
production cost p
co

of the commodity. Each seller
KDV]HURPDUJLQDOSUR¿WLQVXFKDVFHQDULR$WWKLV
point, the sellers realize that they can make more
SUR¿WE\LQFUHDVLQJWKHSULFHRIWKHFRPPRGLW\WR
attract A-type buyers instead of charging p
co
to
attract B-type buyers. Therefore, the sellers reset
their prices to a high value and another cycle of
the price war ensues.
The drawback of the MO algorithm is that it
charges a uniform price to all buyers irrespec-
tive of the buyers’ preferences. However, this
uniform pricing results in a loss of revenue from
A-type buyers who are willing to pay a much
higher price for a commodity than B-type buy-
ers. Thus, the buyer population can be segmented
into different clusters depending on the buyers’
preferences, and a different price can be charged
for each segment.
Although some online merchants such as Ama-
zon have implemented dynamic pricing, it is yet to
be adopted widely in e-commerce. The principal
drawback of the dynamic pricing mechanism that
those merchants have employed is that it offers
identical products to different buyers at different
prices, resulting in discontented buyers. A better
pricing strategy would be to identify the preferred
attribute of different buyers and charge a slightly
different price for the product based on a buyer’s

preferred attribute as described below for the
multiattribute dynamic-pricing algorithm.
Multiattribute Dynamic Pricing
As shown in Figure 1, a buyer compares the prices
offered by different sellers based on his or her
preferred product attribute. To make a competitive
offer in response to a buyer’s purchase request,
DVHOOHULGHQWL¿HVWKHEX\HU¶VSUHIHUUHGDWWULEXWH
to offer a competitive price to the buyer on that
attribute. The seller estimates the distribution
f
pa
of a buyer’s preferences over the product at-
tributes and then uses it to predict the preferred
attribute of a buyer in response to the buyer’s
purchase request.
The algorithm for multiattribute dynamic
pricing is based on FROODERUDWLYH¿OWHULQJ&)
which enables a seller to predict a buyer’s pre-
IHUUHGDWWULEXWH&ROODERUDWLYH¿OWHULQJDOJRULWKPV
(Kleinberg & Sandler, 2003; Sarwar, Karypis,
)LJXUH3ULFHYVWLPHSUR¿OH RI WKUHHVHOOHUV
XVLQJWKH02DOJRULWKP
398
Dynamic Pricing for E-Commerce
Konstan, & Reidl, 2001) collect potential buyers’
opinions or preferences on products of interest, and
recommend possible products to new or returning
buyers. A seller’s attribute-prediction algorithm
for a potential buyer must adaptively respond to

changes in the buyer’s preferences. The buyer
attribute-prediction algorithm described below
achieves this adaptive response by dynamically
updating the seller’s model of the buyer’s attribute
preferences.
Buyer Attribute-Prediction Algorithm
In the buyer attribute-prediction algorithm, a seller
constructs one buyer cluster for each product at-
tribute. Suppose the seller maintains C clusters.
A buyer with preferred attribute a
i
is placed into
cluster c
i
with probability w
i,t
during interval t.
These probabilities are updated dynamically in
response to the buyer’s accepting or rejecting
offers made by the seller. When a buyer makes
a purchase request, the prediction algorithm
takes the history of w
i,t
– s and outputs the pre-
dicted cluster (preferred attribute) for the buyer.
Sophisticated, rather complex algorithms have
been developed for assigning buyers to clusters,
determining appropriate prices for buyers within
clusters, and revising assignments and prices in
response to decisions by buyers to purchase or

not (Dasgupta & Hashimoto, 2004).
FUTURE TRENDS
7KH FROODERUDWLYH¿OWHULQJDOJRULWKP GHVFULEHG
above enables online sellers to determine a buyer’s
preferences over multiple product attributes and
WRXSGDWHWKHSRVWHGSURGXFWSULFHVHI¿FLHQWO\LQ
a competitive market. More powerful learning
techniques such as Q-learning (Mitchell, 1997)
and multi-objective, evolutionary algorithms
(Coello, Veldhuizen, & Lamont, 2002) offer
PHFKDQLVPVWRHQDEOHVHOOHUVWRVHDUFKWKHSUR¿W
ODQGVFDSH PRUH HI¿FLHQWO\ 7KHUH DUH YDULRXV
trade-offs between the rapidity and accuracy of
such learning algorithms. A naive but fast learn-
ing algorithm might compare favorably against a
complex and accurate but slow learning algorithm
in a dynamic environment like a competitive
online market.
An interesting scenario arises when buyers’
purchase preferences are dependent on the prices
being charged by sellers. In such a scenario, a
seller can attempt to learn not only the temporally
varying buyer purchase-preference distribution,
but also the variation in that distribution. Proba-
bilistic algorithms such as hidden Markov models
and moving-target functions that estimate the de-
pendence between temporally varying functions
might be applied in such an environment.
CONCLUSION
We have described different algorithms that an

online seller can use for the dynamic pricing of
products in a posted-price market, where the seller
announces the price of a product on its Web site.
We have also described techniques that an online
seller can use to determine the price of a product,
including multiattribute dynamic pricing and
adaptive response, in which the seller’s model
of the buyers’ attribute preferences is updated
dynamically.
REFERENCES
Brooks, C., Gazzale, R., MacKie-Mason, J.,
& Durfee, E. (2003). Improving learning per-
formance by applying economic knowledge.
Proceedings of the Third ACM Conference on
Electronic Commerce (pp. 252-253).
Brown, J., & Goolsbee, A. (2000). Does the In-
ternet make markets more competitive (NBER
Working Paper No. 7996)? National Bureau of
Economic Research, Massachusetts.
399
Dynamic Pricing for E-Commerce
Chavez, A., & Maes, P. (1996). Kasbah: An agent
marketplace for buying and selling goods. Pro-
ceedings of the First International Conference
on the Practical Application of Intelligent Agents
and Multi-Agent Technology (pp. 75-90).
Clark, D. (2000). Shopbots become agents for
business change. IEEE Computer, 33, 18-21.
Coello, C., Veldhuizen, D., & Lamont, G. (2002).
Evolutionary algorithms for solving multi-ob-

jective problems. New York: Kluwer Academic
Publishers.
Dasgupta, P., & Hashimoto, Y. (2004). Multi-at-
tribute dynamic pricing for online markets using
intelligent agents. Proceedings of the Third Au-
tonomous Agents and Multi-Agents Conference
(pp. 277-284).
Dasgupta, P., & Melliar-Smith, P. M. (2003). Dy-
QDPLFFRQVXPHUSUR¿OLQJDQGWLHUHGSULFLQJXVLQJ
software agents. Journal of Electronic Commerce
Research, 3(3-4), 277-296.
Kephart, J., Hanson, J., & Greenwald, A. (2000).
Dynamic pricing by software agents. Computer
Networks, 32(6), 731-752.
Kleinberg, J., & Sandler, M. (2003). Convergent
D O J R U L W K P V I R U F RO O D E R U D W L Y H ¿ O W H U L Q J  Proceedings
of the Fourth ACM Conference on E-Commerce
(pp. 1-10).
Mitchell, T. (1997). Machine learning. McGraw
Hill.
Nelder, J., & Mead, R. (1965). A simplex method
for function minimization. Computer Journal,
7, 308-313.
Sandholm, T., Suri, S., Gilpin, A., & Levine, D.
(2002). Winner determination in combinatorial
auction generalizations. Proceedings of the First
International Conference on Autonomous Agents
and Multi-Agent Systems (pp. 69-76).
Sarwar, B., Karypis, G., Konstan, J., & Reidl, J.
,WHPEDVHGFROODERUDWLYH¿OWHULQJUHFRP-

mendation algorithms. Proceedings of the Tenth
International WWW Conference (pp. 285-295).
KEY TERMS
Auction: A type of market in which sellers
post an initial price for the item being offered and
a deadline by which the item needs to be sold.
Buyers make bids on the offered item. The auc-
tion mechanism determines the dynamics of the
prices bid by the buyers, the winner-determination
strategy, and the bid-disclosure strategy. Common
auction mechanisms include the English auction,
Dutch auction, and Vickrey auction.
Buyer’s Reservation Price: The reservation
price of an item for a buyer is the maximum unit
price that the buyer is willing to pay for an item.
The buyer’s reservation price is typically drawn
from a uniform or normal distribution.
Collaborative Filtering: A technique that is
used to collect user opinions or preferences for
items of interest. A CF algorithm employs a cor-
relation method to predict and recommend items
to new or returning users based on the similarity
of their interests with those of other users.
E-Commerce: Consists of techniques and
algorithms used to conduct business over the
Internet. Trading processes such as supply-chain
management, strategic purchase planning, and
market mechanisms for trading commodities
online are implemented using e-commerce.
Intelligent Agent: Performs tasks that are

given to it without continuous supervision. An
agent can perceive changes in its environment and
can perform actions to accomplish its tasks.
400
Dynamic Pricing for E-Commerce
Marketplace: A type of a market that corre-
sponds to a central location that enables buyers and
sellers to rendezvous. A marketplace is typically
implemented as a blackboard where sellers post
information about items being offered. Buyers
make offers to sellers, and sellers respond with
counteroffers.
Pricebot: An intelligent agent that is used by
DQRQOLQHVHOOHUWRGHWHUPLQHDSUR¿WPD[LPL]LQJ
price for a product that it sells. A pricebot encap-
sulates the pricing algorithm used by an online
seller and enables a seller to maintain an edge
over its competitors in a dynamically changing
market scenario.
Seller’s Production Cost: The production
cost of an item for a seller includes the manufac-
turing and procurement costs for the item, and
corresponds to the minimum price that the seller
can charge for the item.
Shopbot: An intelligent agent that enables
online buyers to determine and compare prices
and other attributes of products from different
online sellers.
This work was previously published in Encyclopedia of E-Commerce, E-Government, and Mobile Commerce, edited by M.
Khosrow-Pour, pp. 247-252, copyright 2006 by Information Science Reference (an imprint of IGI Global).

401
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 2.6
Planning and Designing
an Enterprise Wide Database
Systems for E-Business
Alexander Y. Yap
Elon University, USA
ABSTRACT
The planning and development of an enterprise-
wide electronic database system for e-business
usually calls for the re-engineering of information
processes coupled with a push toward data content
standardization across the entire organization.
In this chapter, the case study involves a multi-
national conglomerate that is in the process of
integrating and Web-enabling their enterprise
database systems. The objective of the system
was to help engineers sift through millions of
components offered by various suppliers and
component manufacturers, where the end-result
ZDVWRLPSURYHGWKHLQWHJUDWLRQDQGHI¿FLHQF\
of the product development, engineering design,
e-sourcing, and e-procurement processes. This
research is a qualitative action research study on
how different organizational, social, political, and
WHFKQLFDOIRUFHVLQÀXHQFHGWKHVRFLDOFRQVWUXF-
tion of an enterprise-wide information system.
Understanding the dynamics and power of these
socio-technical forces in shaping the development

environment and change process of enterprise
systems is the focal point of this chapter’s dis-
cussion.
INTRODUCTION
In mid-2001, Invensys, a multi-billion dollar
multi-national corporation initiated a project to
implement an enterprise-wide electronic database
system accessible via the Web. The envisioned
database was to form part of the corporation’s
growing e-business system. This database was
geared toward helping engineers sift through
millions of electrical and mechanical components
offered by various suppliers and component
vendors. The database system was envisioned
to be integrated with their e-procurement sys-
tem, product data management systems (PDM),
402
Planning and Designing an Enterprise Wide Database Systems for E-Business
enterprise resource planning (ERP) systems,
and computer-aided design and manufacturing
(CAD/CAM) systems.
The objectives of initiating this enterprise-
ZLGH V\VWHP ZHUH WR VLJQL¿FDQWO\LPSURYH
the product development process by providing
Invensys engineers a better and faster means of
identifying/choosing product components and
cutting product development cost (by lowering
product development errors caused by sub-stan-
dard components); (2) to improve e-sourcing (or
the online search process for the right suppliers)

by having access to a much wider range of sup-
plier catalogs internationally and locally and be
able to compare/analyze them; and (3) to improve
the e-procurement process and lower procure-
ment cost.
7KHFRQWULEXWLRQDQGVLJQL¿FDQFHRIWKLV
research is to provide meaningful insights into
different socio-technical milieus and their pivotal
LQÀXHQFHLQWKHVKDSLQJRIDQHZHQWHUSULVHZLGH
system. Although systems requirements and
functionality are delineated and envisioned at the
onset of system planning, the resulting system is
often implemented and developed differently from
what was initially planned due to the underlying
V R F L R  W H F K QLFD O U H D O LW L H V W K D W V X U I D F H D Q G L Q ÀX H Q F H 
the systems planning as more stakeholders and
systems users become involve in it. Mitigating
socio-technical factors ultimately determine
the path of systems development and adoption.
Therefore, it is important that more studies and
research are conducted to shed light on how these
underlying forces come into play when shaping
enterprise systems.
,QYHQV\VLVDGLYHUVL¿HGFRQJORPHUDWHWKDW
manufactures and provides various products
and services. In the United States, Invensys is a
leading global provider of heating systems, air
conditioning, building systems, and commercial
refrigeration. In Europe, Invensys acquired the
AVP group of companies which specializes in

engineering processes (such as brewery and dairy
V\VWHPV'XHWRWKHFRQJORPHUDWH¶VGLYHUVL¿HG
global business, it was of great interest to pursue
this study to see how various Invensys subsidiar-
LHVZRUOGZLGHFRXOGEHQH¿WIURPRUFKDQJHZLWK
a new enterprise-wide system. What was also
interesting from a socio-technical research point
of view was that at the time of this project’s imple-
mentation (2001-2002), Invensys Corporation just
acquired Baan, a leading ERP solutions provider
(Baan was re-acquired by another company in
2003). This created an underlying situation that
Baan, being an enterprise solutions provider,
ZRXOGKDYHFRQVLGHUDEOHLQÀXHQFHLQWKHGLUHFWLRQ
of this enterprise-wide project.
RESEARCH INTEREST AND
APPROACH
The objective of this research is to determine the
key factors that affect the planning and develop-
ment of enterprise-wide systems. We were hired
as information systems consultants tasked to plan
and design the implementation of this project from
2001-2002. When hired, we realized it was a great
research opportunity, because it would allow us
to experience how an enterprise-wide systems
development goes through the intricacies of a con-
glomerate environment. The retrospective value
of this research was discussed and agreed upon
with Invensys Technology vice president, Tim
Matt. This research is a result of our documented

analyses and insights as to how we strategically
planned and tactically developed the system on a
day-to-day basis considering the organizational,
social, political, and environmental forces that
ultimately shaped the systems design. We want
to continue the discourse of Kim, Lee, and Go-
sain (2005) and Gosain (2004) who claimed that
enterprise information systems are subjected to
institutional forces and processes, and Soh and
.LHQZKRGLVFXVVHGWKHQHHGWR¿WFXOWXUH
with enterprise systems solutions or there will
be gaps that could lead to mismatch between the
solution and the enterprise’s needs.
403
Planning and Designing an Enterprise Wide Database Systems for E-Business
Since the researchers were involved in the proj-
HFWWKLVFKDSWHULVFOHDUO\FDWHJRUL]HGDV³action
research”. While quantitative methods are good
for some type of research, we argue that qualita-
tive research is the more appropriate approach to
determining the casual effects of socio-technical
and organizational factors in shaping the develop-
ment of enterprise-wide systems.
1XPHURXVVWXGLHVLQWKH¿HOGRILQIRUPDWLRQ
V\VWHPVKDYHDFNQRZOHGJHGWKDW³DFWLRQUHVHDUFK´
is a well-suited method for dissecting the complex
social dimensions of IS planning and development.
Previous action research methodology studies by
Baskerville (1999), Wood-Harper (1985), and Hult
and Lennung (1980) discussed action research

as appropriate for understanding the social set-
ting of the information systems environment. To
quote Baskerville on his adoption of Hull and
/HQQXQJ¶VGH¿QLWLRQRIDFWLRQUHVHDUFKKHFLWHG
four major characteristics of the action research
methodology:
1. Action research aims at an increased under
-
standing of an immediate social situation,
with emphasis on the complex and multi-
variate nature of this social setting in the
IS domain.
2.
Action research simultaneously assists in
practical problem solving and expands sci-
HQWL¿FNQRZOHGJH7KLV JRDOH[WHQGVLQWR
two important process characteristics: First,
there are highly interpretive assumptions
being made about observation; second, the
researcher intervenes in the problem set-
ting.
3. Action research is performed collabora
-
tively and enhances the competencies of
the respective actors. A process of partici-
patory observation is implied by this goal.
Enhanced competencies (an inevitable result
of collaboration) is relative to the previous
competencies of the researchers and subjects,
and the degree to which this is a goal, and

its balance between the actors, will depend
upon the setting.
4. Action research is primarily applicable for
the understanding of change processes in
social systems.
Although action research can be viewed as
subjective, the intention of this research is to
learn from experience, so we will not attempt to
cloud it with our biases as we, too, want to fully
learn from facts and events that transpired. We
accepted the consulting role based on the op-
portunity to learn more about enterprise-wide
systems planning and development. Baskerville
VWDWHGWKDW³FRQVXOWDQWVDUHXVXDOO\SDLG
to dictate experienced, reliable solutions based on
their independent review. Action researchers act
RXWVFLHQWL¿FLQWHUHVWWRKHOSWKHRUJDQL]DWLRQLWVHOI
to learn by formulating a series of experimental
solutions based on an evolving, untested theory.”
As academic researchers taking on the consultant’s
role, we fully concur with this statement.
THEORETICAL FRAMEWORK:
SOCIO-TECHNICAL FACTORS
SHAPING ENTERPRISE-WIDE
SYSTEMS
There are internal and external factors affecting
the way enterprise-wide information systems
(EIS) are planned and developed. Figure 1 maps
WKHVHLQÀXHQFLQJIDFWRUV$OWKRXJKVRFLRFXOWXUDO
forces (Bijker, Hughes, & Pinch, 1987), political

forces (Robey, 1995), and business process (Ham-
mer & Champy, 1993) are familiar factors that we
occasionally come across as shaping information
systems, it is also abstract as to how these factors
help shape enterprise-wide systems. Our objective
LV WR LQVWDQWLDWH LWV LQÀXHQFH LQ PRUH FRQFUHWH
terms with qualitative data.