b2b ec procurement info transfer analysis measurement
TRANSCRIPT
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Information transfer in B2B procurement:an empirical analysis and measurement
Kyung Kyu Kima, Narayan S. Umanathb,*
aYonsei University, KoreabCollege of Business Administration, University of Cincinnati,
328 Lindner Hall, Cincinnati, OH 45221-0211, USA
Received 15 July 2003; received in revised form 12 May 2004; accepted 3 August 2004
Available online 8 December 2004
Abstract
Inter-organizational relationships employing IT may be the most important technological breakthrough in B2B partnerships,
since it is likely to alter the competitive landscape of industries radically. Electronic integration (EI) may be defined as the
integration of business processes of two or more independent organizations through the exploitation of the capabilities of
computer and communication technologies. Prior research has primarily used the adoption of electronic data interchange (EDI)
as a surrogate measure for EI. While researchers have called for the assessment of the degreeof EI instead of presence/absence of
EDI between two firms, a measure was still to be developed. Conceptualizing EI as a multi-dimensional construct, our researchfocused on developing a measure for a crucial component: electronic information transfer (EIT). Four dimensions of it (decision
and operation integration (DOI), mutual investment in relationship-specific assets (MIRSA), information sharing (IS), and
monitoring and control (MAC)) were analyzed and an instrument for EIT measurement was developed. Data collected from two
major corporations in the U.S. were used to verify the instruments ability to measure EIT effectively.
# 2004 Elsevier B.V. All rights reserved.
Keywords: Inter-organizational systems; Information sharing; Electronic integration; EDI; Electronic information transfer; Supply chain
management
1. Motivation
The use of the Internet to facilitate B2B commerce
has attracted much attention from both academics and
practitioners due to its potential impact on industry
structure and the way business is conducted today[14]. Internet markets have the potential to widen the
choices available to buyers, provide sellers access to a
larger customer base, and slash transaction costs [17].
The B2B markets take different forms (e.g., spot
markets, electronic hierarchies, cooperative arrange-
ments) depending on the characteristics of the
products being exchanged, market variability repre-
www.elsevier.com/locate/dsw
Information & Management 42 (2005) 813828
* Corresponding author. Tel.: +1 513 556 7195;
fax: +1 513 556 6278.
E-mail addresses: [email protected] (K.K. Kim),
[email protected] (N.S. Umanath).
0378-7206/$ see front matter # 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.im.2004.08.004
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sented by market fragmentation and market volatility,
and continuity of the business relationship between the
channel partners.
Existence of production economies favors theemergence of specialized firms interacting in spot
markets. However, in some situations, market con-
tracting can become difficult, increasing the transac-
tion costs of managing the interaction. At some point, it
becomes more efficient to administer the interactions
within a long-term cooperative relationship. Often
environmental and technological factors will make it
possible to increase the overall efficiency of production
or exchange through closer integration of decisions
and operations between the trading partners. However,
this increase in coordination necessarily involves
investment to tailor operations to specific interactions.
When the expected benefits from investments in
coordination minus the cost of this investment are
sufficiently large to counterbalance the loss of an out-
side suppliers production economies, firms can make
investments to gain the benefits of such coordination
[9]. Lack of benefits from explicit coordination often
lead to a transaction-oriented spot market.
One of the key differences in various forms of the
B2B market is the level of integration between the
trading partners. In a spot market transaction, since the
buyers goal is to fulfill an immediate need at thelowest possible cost, minimum integration between
the trading partners is sufficient. Meanwhile, in a
system where transactions occur for a long-term
period in negotiated contracts with qualified suppliers,
a high level of integration between the trading partners
should exist to achieve efficiency. Thus, the level of
integration between the channel partners will vary. On
the basis of organizational information processing
theory [12], one can argue that more is not always
better, especially in electronic integration (EI)
between supply channel partners [18]. Electronicmedia may overload decision makers in a supply
channel with too much information [27]. Inability to
cope with such an information overload leads to
organizational dysfunction. Therefore, the fit
between contextual factors and electronic integration
should be examined to seek optimal channel
performance. An investigation of when tight electro-
nic integration is appropriate and when it is not can
generate strategy prescriptions of significant value to
B2B firms in determining their best level of
deployment of electronic integration appropriate for
their specific inter-organizational relationships.
The first step in our research endeavor was to
develop a means of accurately measuring the degree ofelectronic integration between the trading partners in a
B2B relationship. In the past, several researchers have
called for an assessment of the degree of electronic
integration between two firms [15]. EI may be defined
as the integration of business processes of two or
more independent organizations through the exploita-
tion of the capabilities of computers and communica-
tion technologies [32]. So, we conceptualized EI as a
multi-dimensional construct mainly constituting busi-
ness integration and process integration, and focused
our work on developing a measure for a crucial
component of EI: electronic information transfer
(EIT) which serves as the infrastructure for the inter-
organizational business and process integration. We
define electronic information transfer as a regulated
flow of information between trading partners via
electronic linkages.
2. Existing measures related to electronic
integration
Prior research in EI has used the adoption ofelectronic data interchange (EDI) as a surrogate
measure for EI. Table 1 summarizes the various
definitions of EI and its operationalization in prior
research.
The work asserts that there is a strong- and
mistaken-tendency to equate EI with EDI in existing
research. EI is a broader construct that essentially
subsumes EDI. It caters to two types of integration:
technological interconnectivity issues and business
process interdependence issues [31]. Efforts to
enhance technological interconnectivity have madesignificant strides during the past decade. As EDI
emphasizes technological interconnectivity between
the trading partners, existing measures of it encompass
mainly technological aspects, such as volume,
diversity, breadth, and depth of EDI usage [22].
While such measures serve a definite purpose,
attention to creating interdependent business pro-
cesses is also necessary to allow an organization to
develop a seamless and interoperable technical plat-
form.
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EI often involves interaction with the resources
controlled by the partner firm and requires substantial
mutual adaptation. Thus, any meaningful measure-
ment instrument should capture the degree ofelectronic integration in various dimensions of the
business processes between trading partners. In an
explicitly cooperative relationship, decisions are
coordinated between economic activities through
processes and information that are specific to the
exchange. Thus, we view electronic information
transfer as a significant component of EI.
Several researchers have suggested that EI should
not be viewed as a dichotomous variable but rather as
a step in the integration. A simple EDI link that
automates merely the transmission of orders from a
buyer to a seller does not create any electronic
integration; [8] proposed a scheme to classify the
degree of electronic integration between two firms.
For instance, a basic level of electronic integration
may occur when the linked firms develop product
code translation tables so that employees at the
participating firms can place/receive orders using
internal product codes. A higher level of electronic
integration may be possible when the buyers
computer determines a need for a product, based
on preset reorder levels, and automatically transmits
an order to the suppliers order entry system withouthuman intervention. At the highest level of electronic
integration, the firms can create close electronic
coupling among the processes that create or use the
information being exchanged. Researchers have
called for a way to assess the degree of such
integration instead of measuring just the presence or
absence of EI between firms. The fundamental
activity underlying this phenomenon is information
transfer.
While exchanges between trading partners may
entail movement of goods and services, integration ofbusiness processes intrinsic to these exchanges almost
always involves transfer of information between the
trading partners about the products and processes.
While some products and processes are information-
intensive, all have an informational component.
Business process integration is possible only through
transfer of appropriate informationelectronic or
otherwise. Here, we view information transfer through
the lens of decision and operational integration in an
inter-organizational relationship. On the basis of a
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828 815
Table1
VariousdefinitionsandmeasuresofE
I
Authors
DefinitionofEI
Measures
VenkatramanandZaheer[32]
Integrationofbusinessprocessesoftwoo
rmore
independentorganizationsthroughtheexploitationof
thecapabilitiesofcomputer&communic
ationtechnologies
Adichotomousscalemeasuringwhetheranindependent
insuranceag
entiselectronicallyinterfacedwithaded
icatedsystem
BergeronandRaymond[6]
ThelevelofdiffusionofEDIoutsidethe
organization.
Thelevelofexternalintegrationillustrate
svarious
typesoftradingpartnerswithwhichtheo
rganization
transactsbusinessthroughEDI
Thelevelof
externalintegrationnumberedthediffere
nt
typesoftrad
ingpartnerswithwhichtheorganization
transactsbusinessthroughEDI
ZaheerandVenkatraman[36]
Aspecificformofverticalquasi-integrationachieved
throughthedeploymentofproprietaryinf
ormationsystems
betweenrelevantactorsinadjacentstages
ofthevaluechain
Thepercentageofbusinessdirectedtotheinterfaced
carrier
throughthe
proprietaryelectronicchannel
BensaouandVenkatraman[4]
TheuseofITfunctionalityforfacilitatinginter-organizational
coordination,especiallythenatureandscopeoftheelectronic
linkagesbetweentwomembers
Dichotomou
sitemsmeasuringwhetherdataareexcha
ngedin
electronicfo
rmwiththesupplierin[specific]functions
MassettiandZmud[22]
Thedegreeofelectronicconsolidationthathasbeenestablished
betweenthebusinessprocessesoftwoor
moretradingpartners
Threelevels
of[externalintegration]aredefined:(1)
file-to-fileconnections,(2)application-to-application
exchanges,and(3)coupledworkenvironments
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thesis by [10], we further argue that the electronic
aspect of EIT not only invigorates decision and
operation integration (DOI) but also mitigates the side
effects of decision and operation integration (transac-tion risk)essentially a double-edged (positive)
sword. Therefore, assessment of EIT is an important
issue pertaining to electronic integration in an inter-
organizational relationship.
3. Dimensions of electronic information transfer
The first step in developing a measure is to form a
general conceptual understanding of the underlying
construct. A synthesis of existing literature was used to
aid in the conceptual development. Eventually specific
dimensions of the construct were derived and
empirically validated.
3.1. Theoretical foundation
Transaction cost economics (TCE) asserts that
specialized firms interacting in a market lead to
production economies. However, this entails transac-
tion costs. Risk of opportunistic behavior is a cost of
conducting market transactions [35]; however, the
informational aspect of the transaction cost in a marketis the coordination costs. Hierarchies, on the other
hand, sacrifice production economies in order to
reduce transaction costs. According to TCE, the most
efficient governance of such interactions is achieved
by balancing production economies and transaction
costs.
Cooperative relationship has been considered to be
an extension to the usual dichotomous view of markets
and hierarchies of TCE analyses. A move to the
middle between the polarities of markets and
hierarchies is being proposed as opposed to a moveto the market hypothesis. In fact, [21] states that the
emergence of tightly coupled electronic hierarchies
facilitated by IT tends to support a move to the
middle argument better than a move [exclusively]
to the market position. Breaking down the costs of
cooperation as costs of coordination and costs of
transaction risk (a synthesis of views), it can be argued
that IT can reduce both these costs simultaneously.
EIT is the principal conduit through which IT is
harnessed to accomplish this (Fig. 1).
3.2. Coordination of decision and operation
integration (DOI)
Costs of coordination are often explicated in termsof coordinating decisions and operations among
economic activities that occur between partnering
firms. EIT can facilitate coordination of decisions and
operations by reducing the costs of accumulating,
communicating, and processing information [2]. Thus,
decision and operation integration is proposed as a
dimension of EIT.
In order to identify the decision and operational
activities that occur when trading partners exchange
goods, five basic activities in the purchasing cycle
were identified from the literature: order products,
receive/store products, quality assurance, vendor
invoices, and payments. All these can be facilitated
by EIT. Table 2 presents the features of EIT able to
facilitate coordination in inter-organizational business
operations and decisions.
3.3. Management of transaction risk
Transaction risk is involves opportunistic behavior
by a trading partner, leading to uncertainty surround-
ing the level and division of the benefits from the
increased integration of decisions and operations. Thefocus of TCE has historically been on risks generated
by transaction specific capital [34]. However, efforts
to build a greater degree of integration of decisions
and operational activities while reducing costs of
coordination, exacerbate transaction risk. Information
asymmetries and loss of resource control have also
been identified as possible sources of transaction risk
that result from greater integration of decisions and
operations.
3.3.1. Mutual investment in relationship-specificassets (MIRSA)
Relationship-specific assets are investments that
have little or no value other than in the specific
interaction in which it occurs. Reciprocal investments
in the inter-organizational relationship by the parti-
cipating partners offer an alternative to vertical
integration as a means of safeguarding transaction-
specific assets. One form of reciprocity occurs when
each party makes a nonredeployable transaction-
specific investment, signaling a commitment to the
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continuation of the relationship; this will lower the
likelihood of opportunistic behavior. Regarding IT
assets, transaction risk is reduced to the extent that the
two parties invest in specific resources to leverage IT
capabilities and restructure the nature of the relation-
ship. In the context of electronic information transfer,
these refer to investments in the underlying hardware,
software, and communication systems as well as in
providing user training and support [36]. Specialized
investment in inter-organizational IS (IOIS) for a
buyer-seller dyad seems to facilitate joint programs
and activities and provides a more positive transaction
climate in the dyad as long as the balance of power is
not altered significantly [23]. Thus, cooperation rather
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828 817
Fig. 1. Dimensions of electronic information transfer.
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than competition may make more sense for investment
decisions [9]. Kydd and Jones offered specific
guidelines for creating condusive conditions for
successful implementation of shared information
systems [19]. Relationship-specific skills have also
been documented as a significant investment [29].
While a trading partners investment in relation-
ship-specific assets signals commitment to the
relationship, IT assets possess the unique property
of softening the transaction risk of the trading partners,
viz., switching costs or redeployability. In the current
business environment, firms often may not make
separate investments in computer hardware and
telecommunication equipment for a specific trading
partnership (see items 6 and 7 in Table 3) because the
firms IT infrastrcuture will invariably include such
equipment. Even if a relationship-specific investment
is made by a firm because it is an initial investment,
unless it pertains to specialized equipment, the
durability of the investment as a nonredeployableasset of the specific relationship is questionable.
However, the uniqueness of IT assets in lending some
degrees of freedom in redeployment softens the
transaction risk for both parties. Nonetheless, some or
all such IT investments may also possess a degree of
relationship-specificity in an inter-organizational
relationship. Additionally, the characteristics of
modern software, such as modularity and replicability,
open standards, and intuitive interefaces, render it at
least partially flexible.
Thus, IT investments in EIT affects transaction risk
of both partners in the inter-organizational relation-
ship, but in different ways.
3.3.2. Reduce information asymmetries
Information asymmetry occurs when either partner
in the relationship has privy to information specific to
the relationship that the other do not. Such asymme-
tries increase transaction risk when integrating
decisions and operation in the trading partnership.
A view based on markets and hierarchies emphasizes
the informational aspects of information asymmetry,
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828818
Table 2
EIT in coordination of decision and operation integration
Purchasing Cycle Facilitating EIT features
Order products 1. Use common product codes2. Place purchase order electronically
Quality assurance 3. Trace product failures back to the
offending component(s) electronically
Vendor invoices 4. Receive suppliers invoices electronically
Payment 5. Make electronic payments
Table 3
Role of EIT in managing transaction risk
Management of transaction risk using EIT Facilitating EIT features
Mutual investment in relationship-specific assets 6. Reciprocal investment in communication network
7. Reciprocal investment in hardware
8. Provide IT training
9. Provide technical support
10. Provide customized support
11. Help supplier develop their own software
Information Sharing 12. Exchange production (or sales) data with the supplier electronically
13. Use the data, electronically transferred from the trading partner,
in business decisions14. Vendor-managed inventory (VMI)
15. Share promotion plans with the trading partner electronically
Monitoring and control 16. Access the suppliers shipping/delivery schedule
17. Access the suppliers production schedule
18. Access the suppliers inventory levels of finished products
19. Access the suppliers inventory levels of raw materials
20. Provide performance feedback
21. Search for alternative suppliers for the products
22. Monitor the order status
23. Monitor the suppliers production capacities
24. Monitor the quality of the products being produced
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while economic theories (e.g., TCE, agency theory)
focus on the opportunistic aspect of the phenomenon.
3.3.2.1. Information sharing. Information asymme-try can be reduced when partners freely share
information relevant to the relationship, e.g., electro-
nic exchange of production/sales data, sharing
promotion plans, vendor-managed inventory (VMI),
etc. EIT, by virtue of its inherent capabilities, is a
powerful agent in facilitating information sharing (IS).
Characteristics were derived from this and are
included in Table 3.
3.3.2.2. Monitoring and control. Information asym-
metry can prompt opportunistic behavior (e.g.,
shirking) by trading partners. The difficulty in
measuring the specific contribution of inputs in
generating outputs creates an opportunity for perfor-
mance shirking by the supplier. If the buyer lacks the
ability to monitor the status of the suppliers
production process (the production capacities, inven-
tory levels, shipping/delivery schedule, quality of the
products being produced, etc.), the supplier can reduce
its effort level. Opportunism can also occur in the
presence of a limited number of suppliers, since this
increases the dependency of a buyer on a specific
supplier. The consequent difficulties in performancemonitoring entail transaction risk. While the tradi-
tional economic theories tend to ascribe opportunistic
behavior to the supplier, the transaction risk in a
cooperative relationship can be bidirectional. Once
again, IT is capable of monitoring and controling the
transactions as a byproduct of normal operations in the
EIT environment. Features that pertain to monitoring
and control (MAC) were derived from this rationale
and are also included.
4. Research method
Based on these constructs, an instrument to
measure EIT was developed. The initial questionnaire
contained 24 items representing the four conceptua-
lized constructs: decision and operation integration,
mutual investment in relationship-specific assets
(MIRSA), information sharing, and monitoring and
control. A pilot test was performed to fine-tune the
instrument and to establish face validity. A compre-
hensive instrument validation procedure followed to
validate the instrument empirically.
4.1. Sample
Data were collected from two corporations in the
U.S., each a major player in its industry segmentone
is in the retail grocery industry and the other is a
machine tool manufacturer.
A set of interviews were initially conducted with
senior managers responsible for purchasing since this
is the boundary spanning function considered to be
most critical in supplier relations. The interviews:
(1) provided a preliminary corroboration of the
applicability and appropriateness of the EIT
construct; and
(2) ensured that we had an adequate base to sample
the relationships covering the vast array of
suppliers and products.
Through these interviews, we were directed to appr-
opriate buyers.
The unit of analysis in our study pertains to the
inter-organizational relationship-more specifically p-
airs of product categories and suppliers. A product
category refers to a group of products with similar
characteristics and it already existed in the organiza-tions sampled. Typically most buyers were responsible
for multiple product categories and often used multi-
ple suppliers for each category. Each buyer was asked
to answer a questionnaire that measured the dimen-
sions of EIT. A data point in this study consisted of a
dyad made up of a product category and a supplier.
Altogether, 39 buyers from eight offices throughout
the U.S. participated in this study and all handled
multiple product categories. The final sample had 160
data points, i.e., 160 product category-supplier dyads.
4.2. Pilot test
Preliminary testing involved structured interviews
and a pretest carried out using responses from six
buyers. The multiple structured interviews conducted
with managers of purchasing divisions were intended
to test the face validity of the instrument. Based on the
feedback from these interviews, a few questions were
rephrased to reflect industry specific situations better.
Then, the six buyers were asked to indicate their
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responses to each of the 24 questions, which had a
seven-point Likert scale with values from Not at all to
Very much so. The pretest yielded 32 data points on the
{product categorysupplier} dyad from the sixbuyers. Based on this pretest, some questions were
modified to enhance clarity.
4.3. Exploratory factor analysis
Principal components analysis provided prelimin-
ary verification of the four constructs. Table 4 provides
the standardized parameter estimates (factor loadings)
of the items over the four dimensions. We used the rule
of thumb of 0.50 as the cut off value for factor
loadings. These four factors accounted for 60.4% of
the variance in the data set.Four out of the five items theoretically developed to
reflect the construct of decision and operation
integration loaded on a single factor. Item 3 did not
load on any factor and was discarded from further
consideration. All six items expected to measure the
mutual investment in relationship-specific assets
loaded on a single unique factor. Only two out of
the four items expected to represent the information
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828820
Table 4
Factor loadings of the EIT measure
Defined items Derived
Factor 1 Factor 2 Factor 3 Factor 4
Decision and operation integration (DOI)
Item 1 0.155 0.185 0.600* 0.071
Item 2 0.126 0.150 0.640* 0.073
Item 3 0.202 0.092 0.021 0.221
Item 4 0.078 0.074 0.739* 0.136
Item 5 0.336 0.039 0.516* 0.175
Mutual investment in relationship-specific assets (MIRSA)
Item 6 0.767* 0.004 0.108 0.185
Item 7 0.810* 0.110 0.030 0.070
Item 8 0.779* 0.054 0.082 0.359
Item 9 0.832* 0.037 0.163 0.224
Item 10 0.744* 0.125 0.146 0.341
Item 11 0.668* 0.023 0.089 0.337
Information sharing (IS)
Item 12 0.084 0.105 0.128 0.679*
Item 13 0.341 0.260 0.312 0.017
Item 14 0.238 0.016 0.066 0.555*
Item 15 0.593* 0.226 0.030 0.211
Monitoring and control (MAC)
Item 16 0.047 0.746* 0.013 0.124
Item 17 0.038 0.327 0.330 0.291
Item 18
0.004 0.779*
0.187 0.077
Item 19 0.091 0.812* 0.120 0.068
Item 20 0.389 0.510* 0.131 0.220
Item 21 0.238 0.082 0.262 0.408
Item 22 0.115 0.834* 0.138 0.061
Item 23 0.027 0.520* 0.366 0.087
Item 24 0.077 0.437 0.425 0.038
Eigenvalue 5.5 4.26 2.63 1.51
Percentage of variance explained 25.83 20.38 8.05 6.18
Cronbachs Alpha 0.93 0.85 0.78 0.67
Note: Extraction method = principal component analysis; rotation method = varimax rotation. Cutoff eigenvalue: 1.0.* Indicates the highest loadings greater than 0.5.
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sharing construct loaded on a single factor. One (item
13) did not load on any of the four factors and the other
(item 15) exhibited a significant loading on the
MIRSA dimension. We discarded this item asspurious, perhaps this was a specific result for this
data set, since we could not find any reason that
explained such behavior.
Finally, six out of the nine items defining the
construct of monitoring and control loaded together.
The other three (items 17, 21, and 24) did not exhibit
any relationship with any of the other factors and were
removed from further analysis. Thus, the final
instrument consisted of a four-factor structure with
18 scale items. A copy of this instrument appears in
the Appendix A.
4.4. Instrument validation
A comprehensive instrument validation procedure
suggested by [11] was then followed. Structural
equation modeling (SEM) was used to test construct
validity, which may be defined as the correspondence
between a construct and the operational procedure to
measure or manipulate that construct [25]. Confirma-
tory factor analysis (CFA) is considered well suited to
investigate constructs can be distinguished from one
another [20]. Thus, the CFA procedure was used todetermine whether the EIT items in the instrument
adequately represented the hypothesized dimensions.
The analyses used are summarized in Table 5. The
structural equation modeling tool, EQS, was used to
conduct the analysis.
Sample size is an important consideration in
determining the appropriateness of estimating a
CFA model [16]. Based on the sample size to
parameter estimate ratio of 5 suggested in [5] as the
minimum sample size, we concluded that our sample
size was adequate.
4.5. Results
The pattern of factor condensation is the first
indicator of the degree of convergence. The factorloadings of the measurement items depicting the
theoretically derived constructs strongly ratify con-
vergent validity of the instrument. In CFA, it is
essential first to examine the overall fit of the model. If
a model does not fit the data, the hypothesis that the
model accurately represents the data is rejected.
However, because the underlying population distribu-
tions for these statistics are unknown and because
there is no clear consensus in what constitutes an
appropriate fit, assessment of overall model fit is still a
subjective process [30].
Pursuant to the conceptual development, we
formulated the measurement model as a four-factor
structure: DOI, MIRSA, IS, and MAC. The fit
statistics of this model were: comparative fit
index = 0.80, BentlerBonnet normed fit index = 0.75,
nonnormed fit index = 0.77. While rule of thumb value
of 0.90 for the indices has been suggested [24], we
decided to retain the model because the pattern of
factor condensation was robust (see Table 4). Further
the fit indices are reasonably close to the rule of thumb
value. These fit statistics and parameter estimates
provided further evidence of the convergent validity ofthe items measuring the four dimensions of EIT.
Discriminant validity was assessed using two
different methods. The first was to see whether the
scale items were capable of differentiating the
multiple dimensions of EI (Table 5, item 3). This
was achieved by comparing the fit of the hypothesized
four-factor model to a model with a single EIT
construct. The discriminant validity would be estab-
lished when the fit of a single factor model was
significantly different from the hypothesized model. In
such a case, a single factor model would beinsufficient to capture the multidimensionality of
the EIT. Our data indicated that the fit of the single
factor model was significantly different from the
hypothesized four-factor model (p < 0.001). This
result provided support for the discriminant validity
of the proposed dimensions of EIT.
It can also be assessed by examining whether
measures of purportedly different constructs display
differential patterns of correlations with other external
construct(s). We used EDI as the external construct
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828 821
Table 5
Summary of analysis
Analysis Research objective
Significance of factor loadings Convergent validity
Fit of the four dimensional model Convergent validity
Alternative (single factor)
measurement models
Discriminant validity
Differential correlations between EIT
dimensions and EDI
Discriminant validity
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(Table 5, item 4) and considered EDI as appropriate to
test the discriminant validity of the EIT instrument,
since it has often been used as the surrogate for EI in
prior research. If EDI relates similarly with all the fourdimensions of EIT, it calls into question the utility of
distinguishing among the four dimensions.
The test procedure entailed estimating two models
and comparing them. One allowed the correlations
between EDI and each of the four EIT dimensions to
be freely estimated; the other constrained the
correlations between EDI and the dimensions of
EIT to be equal. If the fits of these two models were not
significantly different, then EDI would be construed to
have a similar association with all the dimensions,
essentially refuting the utility of distinguishing among
the four dimensions. We developed a four-item scale
to measure EDI. It pertained to EDI tasks in document
exchanges in EIT. The reliability of the EDI scale, as
indicated by Cronbachs coefficient alpha (0.86), was
quite robust.
The results showed that constraining EDI to have
the same relationship with each dimension resulted in
a significant difference from those of the unrestricted
model (p < 0.001). This indicated that the dimensions
of EIT indeed exhibited different relationships from
EDI, thus, supporting discriminant validity of the
derived dimensions.
5. Discussion
While several authors have argued that EDI is not
EI and have called for a comprehensive measure that
assesses the degree of EI between firms, none
developed an instrument to measure EI per se. A
major contribution of our research was, thus, the
development of an instrument to measure EIT, the
information transfer/flow infrastructure for EI. Usingthe triangulation of multiple theoretical perspectives
as the foundation, we culled electronic information
transfer, the underlying infrastructure for EI based on
coordination of decisions and operation and transac-
tion risk. We then identified relationship-specific
assets, information sharing, and monitoring and
control as the dimensions of transaction risk.
The four dimensions of the EIT construct were
empirically proved in our study. Convergent validity
of the scale was indicated by the factor condensation
pattern matching the conceptualized dimensions.
Also, results from the CFA procedure further ratified
convergent validity. In terms of discriminant validity,
the hypothesized four-factor model represented thedata significantly better than the aggregated single
factor model. The four dimensions also exhibited
differential patterns of relationships from those of
EDI.
Although our sample size was adequate for the
stability of statistical analyses, the data were collected
from only two companies. The interpretation of the
results is therefore subject to the constraints of
organizational and business characteristics of these
two organizations. Since the task context in this
research is procurement, we sought generalizability
over product categories. The two firms in our study are
major players in their respective industry segment in
the U.S. Therefore, we were able to collect rich data on
product categories.
6. Implications for practice
An example of research incorporating EIT as a
dependent variable is to identify and evaluate the
antecedents of EIT. There has been a stream of
research based on the premise that EI corresponds to ashift away from the market-based exchange toward
more bilateral and cooperative governance. In order to
reduce transaction costs, most retail chains have
traditionally used captive distributors, vertically
integrating to combine retail stores and distribution,
while some manufacturers have vertically integrated
into distribution by providing direct store delivery for
their products [1]. However, recent information
interchange using electronic linkages between firms
has been transforming the nature of the B2B
relationships [26]. Antecedents of EIT cited in theliterature include organizational trust [13], governance
structure of the relationship [3], environmental
uncertainty, and product complexity. This stream of
research can benefit from the instrument developed in
our study.
Since the two companies in the sample belong to
different parts of a supply chain, that is, the retail
grocery company in the downstream chain and the
machine tool manufacturing company in the upstream,
different inter-organizational information system
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(IOIS) capabilities may be needed to realize appro-
priate supply chain coordination [7]. Since an IOIS
provides the fundamental infrastructure for linking
members of a supply chain, proper alignment betweenthe developmental stage of the supply chain and the
integrating capabilities of IOIS is critical [28,33].
From a management perspective, EIT, if properly
deployed, offers the unusual capability of providing
significant benefits to members on both sides of the
partnership. The firm can not only deliver cost savings
to its trading partners, but also enhance the service it
provides while reducing its own costs of operation.
When the firm can reduce its trading partners
transaction costs while simultaneously reducing its
own, the entire supply channel can perform more
effectively. The linchpin in this complex set of
relationships is the extent of EIT between the
participating firms. This research shows why man-
agers ought to invest time and effort in assessing
current and future information exchange with their
supply chain partners and align their IOIS capabilities
accordingly.
Also, electronic linkage between a firm and its
suppliers has opened up new avenues for business
integration between the participants. As a conse-
quence, the very nature of the relationship between the
firm and its suppliers is drastically affected, necessi-
tating a reassessment of the firms business strategy.Managers have been limited to assessing EI via
through technological surrogate, EDI. The instrument
developed here provides practicing managers addi-
tional capabilities in the assessment of EI. Managers
can now validate current industry practices and
generate specific recommendations regarding the
information flow aspect of their supply chain logistics.
The instrument also enables managers to isolate and
examine decision and operation integration, invest-
ments in relation-specific assets, information sharing
and/or monitoring and control aspects of information
transfer/flow infrastructure of the firm and its partners
and strengthens the weaker links.
Acknowledgements
We thank the three anonymous reviewers for their
comments. This research was partially funded by
Yonsei University, Korea.
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K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828824
Appendix A
A.1. EI QuestionnairePart I
Please pick two major product categories you manage that add most value to your company.
For each major product category, list the major suppliers. If the type/level of electronic integration (EI) with any
two suppliers is about the same, you may use only one of them.
Level of EI is a continuum from minimal integration to complete integration. For example, simply sending
purchase orders electronically can be considered as minimal integration. Other forms of EI include vendor managed
inventory (VMI) (you supply data elements to vendors, and you create purchase orders), VMI-Advanced (you
supply data elements to a supplier and the supplier creates purchase orders), VMI-Advanced plus electronic
payments, etc.
Where possible, choose suppliers for a product category with differing degrees of EI.
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K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828 825
A.2. EI QuestionnairePart II {SupplierProduct
Category Dyad}
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Kyung Kyu Kim is a Professor of Infor-
mation Systems at Yonsei University,
Korea. His research interests are in the
areas of B2B e-commerce, supply chain
management, and trust in e-commerce
adoption. He has been a faculty memberat the University of Cincinnati, Pennsyl-
vania State University, and Inha Univer-
sity in Korea. He has published his
research works in Accounting Review,
MIS Quarterly, Decision Sciences, Journal of MIS, Information
and Management, Database and Journal of Information Systems.
Narayan S. Umanath is Professor of
Information Systems at the University
of Cincinnati, Ohio. Entering academia
after fifteen years of technical and man-
agerial experience in software develop-
ment, Umanath received his Ph.D. in
Business Administration from the Uni-versity of Houston in 1987. His under-
graduate and graduate educations are in
mechanical engineering and industrial
engineering respectively. His current research interests include
electronic integration in supply chain relationships, organizational
issues pertaining to Information Systems, and data modeling & data
warehousing. His research publications have appeared in Commu-
nications of the ACM, Decision Sciences, Information & Manage-
ment, Information Resources Management Journal, Journal of MIS,
Journal of Managerial Issues and Management Science.
K.K. Kim, N.S. Umanath/ Information & Management 42 (2005) 813828828