information processing view of organizations: an...
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Working Paper – Not to be quoted
Information Processing View of Organizations: An Examination of Fit in the Context of Supply Chain Management
G. Premkumar@ Professor, MIS
Department of Logistics, Operations, and Management Information Systems College of Business
Iowa State University Ames, Iowa 50011
Tel #: (515) 294-1833 E-Mail: [email protected]
K. Ramamurthy Professor, MIS
School of Business Administration University of Wisconsin-Milwaukee
P.O. Box 742 Milwaukee, WI 53201 Tel #: (414) 229-5809
E-Mail: [email protected]
Carol R. Saunders Professor, MIS
Department of MIS College of Business Administration
University of Central Florida PO BOX 161400
Orlando, FL 32816 Tel #: (407) 823-6392
E-Mail: [email protected]
Please do not quote without the permission of the authors
@ All authors have contributed in the development of the manuscript; however, please direct all future correspondence to the marked author
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Information Processing View of Organizations: An Examination of Fit in the Context of Supply Chain Management
Abstract
Supply chain management (SCM) and Business-to-Business (B2B) E-commerce has attracted significant attention in recent years as the availability of a ubiquitous network linking most businesses has created new opportunities for electronic integration between firms. Companies need to, however, be cognizant of the inter-organizational relationships and the requirements of business processes in their supply chain when choosing from among a wide range of communication technologies and implementation strategies. This study uses Galbraith's (1973) information processing theory to examine the fit between information processing needs and information processing capabilities in an inter-organizational supply chain context and examine its impact on performance. A taxonomy of information processing needs based on inter-organizational transaction characteristics, and information processing capabilities based on IT use in various activities of the procurement life cycle is developed. The impact of the fit between these variables on procurement performance is examined. The study collected data on 142 products through personal interviews and surveys, used cluster analytic techniques to develop taxonomies, and ANOVA to test the fit between needs and capabilities. The results reveal two clusters each in information processing needs and information processing capabilities. ANOVA results show that the interactive effect of needs and capabilities has a greater effect on performance than the main effects, supporting our fit theory. Keywords: Supply Chain, Inter-organizational systems, information processing theory, strategic
fit, B2B, information processing needs, information processing capabilities, cluster analysis
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Introduction Supply chain management (SCM) and Business-to-Business (B2B) E-commerce has attracted
significant attention in recent years as it becomes the next frontier for process reengineering
through extensive information technology (IT) deployment. The availability of a ubiquitous
network linking most businesses has created new opportunities for electronic integration between
firms. The successful deployment of new IT-enabled processes for reducing inefficiencies in
internal operations has motivated firms to examine inter-organizational (I/O) business processes
where there are significant operational inefficiencies. However, these processes span trading
partners with different business objectives and stakeholders. Reengineering the IT enabled I/O
processes is quite complex since the entire supply chain comprising of multiple trading partners
needs to be optimized. Companies need to be cognizant of the inter-organizational relationships
and the requirements of business processes of other companies in their supply chain when
choosing from among a wide range of communication technologies and implementation
strategies. There needs to be a good fit between the technology capabilities and the requirements
of the business process. The initial failures of B2B electronic marketplaces serve as a reminder
that force-fitting technology in incompatible situations will not result in successful outcomes.
Hence, understanding the linkage between technology capabilities, inter-organizational
relationships, and supply chain management requirements has attracted the attention of both
practitioners and researchers (Handfled and Nichols, 2002).
Studies on inter-organizational systems (IOS) have examined a variety of factors influencing
IOS including governance structure, inter-organizational relationships, trust, power, dependence,
competitive pressures and other factors (See Chwelos, Benbasat, and Dexter, 2001 for a review).
Most of these studies have used the deterministic perspective of innovation adoption that new
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IOS technologies are inherently good for organizations. However, there are very few studies that
have attempted to explain IOS implementation from a perspective of fit between information
technology needs and information technology capabilities for inter-organizational interactions.
Galbraith’s (1973) information processing view of organization is one of few theories that
emphasize the notion of fit between a firm’s information processing needs and information
processing capabilities. While most early scholarly work applied the theory to internal
organizational issues, in recent years the theory has been extended to study inter-organizational
interactions. However, empirical validation of the theory in an inter-organizational context is
very limited (Bensaou and Venkatraman, 1995). The dramatic growth in information processing
capabilities for inter-organizational interactions provides a great opportunity for empirically
testing this theory in the new environment. Further, the concept of fit, which is very popular in
strategy research (Venkatraman, 1989), has not been fully explored in IS research except in a
few instances such as research on strategic IS planning alignment (Sabherwal and Chan, 2001).
Thus, an empirical examination of the interactions of inter-organizational relationships,
information processing needs, and new technology capabilities would be a significant
contribution to this stream of research as well as to research on implementation of information
systems.
While studies in other areas have attempted to develop taxonomies on inter-organizational
relationships to better understand inter-organizational networks (Canon and Perreault, 1999), in
IS research we have used existing classifications (Choudhary, 1997) and have not attempted to
empirically validate them. Most classificatory empirical studies fall into two broad categories -
studies that focus on testing hypothesized classification schemes or typologies, and studies that
uncover natural taxonomies (Bensaou and Venkatraman, 1995). Typically, typologies are
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formulated based on theoretical and conceptual foundations and studies empirically test if the
data support the a priori specifications or the hypothesized relationships. This has been
predominant research approach for most MIS research. A common problem with typologies is
that they may not be comprehensive either due to limited focus of the underlying theoretical
foundations or the assumptions that constrain the development of a comprehensive model. While
researchers in marketing have developed taxonomies using cluster analytic techniques to
understand consumer behavior and market segmentation it has not been extensively used in IS
research, except in a few instances (Jain, Ramamurthy, Ryu and Yasai-Ardekani, 1998). This
study’s attempt to empirically develop taxonomies for information processing needs and
information processing capabilities would be a unique contribution to IS research.
The primary objectives of this study are: (a) developing taxonomies of information
processing needs based on inter-organizational transaction characteristics, and information
processing capabilities based on information technology use in various activities of the
procurement life cycle; and (b) formulating and empirically testing the information processing
theory’s concept of fit between the information processing needs and capabilities;
The paper is organized as follows. We initially provide a brief background on the theoretical
foundations for this research followed by a description of the research model with justifications
for the variables. Next, the research methodology, data analytic procedure, and the sample are
described. Finally, the results and their implications, limitations, and extensions are discussed.
Background Research
Research on inter-organizational networks has spanned many disciplines including
organizational theory (Thompson, 1967), transaction cost economics (TCE) (Williamson, 1975),
and marketing (Heidi and John, 1990). One stream of research that is particularly relevant to our
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research is the information processing view of organizations. This theory identifies three
important concepts – information processing needs, information processing capabilities, and the
fit between the two to obtain optimal performance (Galbraith, 1973). Environmental uncertainty
is a constant factor confronting organizations and firms are constantly engaged in reducing
uncertainty with various coping strategies including collecting more information for decision
making (Thompson, 1967). Uncertainty in the environment stems from the complexity of the
environment and the dynamism or the frequent changes to various environmental variables
(Duncan, 1972). Lack of information on these environmental variables and a lack of
understanding of their interactions causes uncertainty in the decision making process. This
uncertainty is distributed within organizational sub-units. Very often, internal sub-units create
additional uncertainty by restricting the flow of information within the organization. Typically,
organizations have two strategies to cope with uncertainty – (a) develop buffers that will reduce
the impact of uncertainty, (b) implement structural mechanisms and information processing
capabilities that enhance the information flow and thereby reduce uncertainty. A classic example
of the first strategy is having inventory buffers to reduce the impact of uncertainty in demand or
supply; another example is adding extra safety buffers in product design due to uncertainty in
product working conditions. An example of the second strategy is the redesign of business
processes in organizations and implementation of integrated information systems that improve
information flow and reduce uncertainty within organizational sub-units. A similar strategy is
being followed to address the uncertainties in the supply chain by creating better information
flow between organizations using inter-organizational systems. Past studies have found that
complexity of the organizational structure and tasks is related to sophistication in information
processing capabilities (Thompson, 1967; Daft and Lengel, 1986).
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While the initial focus of this research stream was on internal organizational structure and
processes, it has been extended to inter-organizational interaction and governance structure
(Bensaou and Venkatraman, 1995). Uncertainty increases significantly when two organizations
that have different business objectives and stakeholders are involved in a transaction. Quite often
the parameters for the transaction may be suboptimal for either of the partners prompting them to
engage in opportunistic behavior to exploit the uncertainty (lack of information) to their benefit.
While ideally one would expect all information for making decisions on a transaction is available
to both parties, typically markets are made with imperfect information accompanied by varying
levels of information asymmetry between the trading parties. In such an environment research
on organizational interdependence and political economy becomes important to understand the
socio-political aspects of the interaction (Stern and Reve, 1980; Thompson, 1967).
Socio-political theory examines inter-organizational interactions based on the extent of
dependence of one trading partner on the other, the power resulting from such dependence, the
extent to which that power is exercised in the relationship, and various other strategies to
improve the inter-organizational relationships (Benson, 1975; Stern and El-Ansary, 1982). Given
the complex relationships between multiple organizations various strategies are developed to
optimize the performance of the relationship. Trust is a critical factor to minimize the risk and
thereby uncertainty in inter-organizational transaction (Hart and Saunders, 1998). Trust in the
partner provides clear expectations on the actions of the partner and reduces the costs incurred in
developing detailed contracts and monitoring compliance to the contract.
Uncertainty is also a central theme in the transaction cost economics (TCE) field, which
focuses on identifying the appropriate governance structure for transactions that minimizes the
total cost. Williamson (1975) initially identified markets and hierarchies as two governance
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structures that firms use to minimize transaction cost. While markets have the advantage of
economies of scale and specialized skills, they suffer from high coordination cost and transaction
risk. Hierarchies, on the other hand, have limited coordination cost and minimal risk, but do not
have the benefits of economies of scale. Uncertainty causes higher transaction costs and the
participating firms will migrate towards a governance structure that minimizes the transaction
cost. Uncertainty could arise through lack of information on their partners and their actions. For
example, firms face the possibility of partners underperforming on their contracts due to lack of
adequate monitoring tools to verify their performance or it could be that they are locked up in a
relationship with a firm through relationship specific investment that increases their switching
costs to transfer to a better partner. These transaction risks or implicit transaction costs have to
be matched with the benefits of better prices in a competitive market. Firms develop long-term
relationships with a few suppliers to minimize the uncertainty but revalidate these relationships
through annual contracts that bring in some of the efficiency of the market. Williamson (1991)
acknowledged the existence of various forms of hybrid governance structures between the two
extremes of markets and hierarchies.
The concept of fit is a core construct in the information processing view of the organization.
Achieving a fit between the information processing needs and the information processing
capabilities to attain optimal organizational performance has been a primary mission of
organizational designers (Galbraith, 1973; Tushman and Nadler, 1978; Daft and Lengel, 1986).
Daft and Lengel (1984) argued that the amount and richness of information processing and
communication media must match with the level of task uncertainty. In a later study (1986) they
expounded the contingency design theory positing that the fit between information needs and
information processing capabilities results in better unit performance. In the inter-organizational
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context the notion of fit was conceptualized and empirically tested first by Bensaou and
Venkatraman (1995). However, no further studies have expanded on that concept or examined
fit in greater detail in IT research. Fit, although intuitive from a theoretical perspective, is an
elusive concept for empirical research. Researchers prefer causal models to contingency models
where multiple variables have to coexist at certain levels for optimal outcomes. While it is easy
to theorize the concept, its operationalization and empirical testing with an appropriate statistical
procedure is still a major issue. Galbraith and Nathanson (1974, p. 266) commented, “Although
the concept of fit is a useful one, it lacks the precise definition needed to test and recognize
whether an organization has it or not.”
Venkatraman (1989) provides an excellent overview of various forms of fit, statistical
methods used for analysis, and the implicit assumptions made in the theoretical formulation and
empirical analysis. His study was primarily focused on strategy research where the concept of fit
is extensively used in developing taxonomies of strategies (Miles and Snow, 1978; Hambrick,
1994), assess internal congruence (Miller and Friesen, 1982), and evaluate fit between strategy
and structure (Chandler, 1961). Although conceptualized in the context of strategy research, it is
equally applicable in other disciplines. In his research, Venkatraman (1989) identifies six
different perspectives of fit. They are: (a) moderation, (b) mediation, (c) matching, (d) gestalts,
(e) profile deviation, and (f) covariation. While “moderation” and “mediation” are traditional
methods to test for causal effects and is extensively used in MIS research, the other methods are
quite rare. In “matching”, fit is a theoretically defined match between two variables, captured
either as a difference between the two variables, or a residual from regression of one variable on
other, or the ANOVA notion of interaction effects of the two variables. Fit as “gestalt”, captures
the internal coherence among a set of attributes by using cluster analytic techniques. Fit as
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“profile deviation” assesses the degree of adherence to an externally specified (or ideal) profile
and deviation scores are used as a measure of fit. The last category, fit as “covariation”,
examines the internal consistency or covariation among a set of underlying dimensions, and is
modeled as a second order factor analysis where the first order defines the dimensions that
together form the second order construct of ‘fit’ among the dimensions.
Research Model
We use the information processing view of organization as the basic foundation for our research
model shown in Figure 1.
<<< Insert figure 1 about here >>>
The research model illustrates the two main constructs of our study, information processing
needs and information processing capabilities, and their impact on performance. Consistent with
information processing theory and the definition of fit, as described earlier, we use the concept of
fit in three contexts - to define the information processing needs, information processing
capabilities, and their joint interaction on performance. First, we develop taxonomies of
information processing needs of an organization based on a variety of theoretically identified
factors that contribute to uncertainty in the interactions. In line with the suggestions of
Venkatraman (1989) for developing taxonomies, we use the ‘fit as gestalt’ notion to identify
categories of organizations based on the internal coherence of a set of attributes that define the
information processing needs of the organization. Prior studies have used a similar approach to
classifying inter-organizational relationships (Bensaou and Venkatraman, 1995; Canon and
Perreault, 1999). The gestalt approach examines feasible sets of internally consistent and equally
effective configurations of multiple variables with minimal assumptions on the relationship
between variables. Typically, an inductive technique such as cluster analysis is used. To build
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rigor into the analysis, based on the recommendations of Venkatraman (1989), with more details
provided later on in the article, we: (a) used formal statistical tools to test the number of gestalts,
(b) demonstrated cluster stability using cross validation of results, and (c) described gestalts
based on the theory that guided the selection of input variables.
The second context of fit relates to technology capabilities in procurement activities.
Organizations use different technologies for different procurement activities driven by IT
capabilities and organizational philosophies. Rather than using a single measure of IT
capabilities employing the ‘fit as gestalt’ notion to identify clusters of organizations with similar
technology use characteristics is a better option to understand the information processing
capabilities
The third context of fit uses the ‘fit as matching’ concept to study the impact of the fit
between information processing needs and information capabilities on performance. This fit
model hypothesizes that the interaction effects of the two variables will have a significant effect
on outcome. Since the two variables are categorical in our study an ANOVA model is used to
test the impact of interaction effects on procurement performance.
Information Processing Needs
Uncertainty is the central construct that drives the information processing needs of the
organization. Since the context of the study is inter-organizational interactions, we use Bensaou
and Venkatraman’s (1995) conceptualization of uncertainty in our model. They identified three
categories of uncertainty, namely, environmental uncertainty, partnership uncertainty, and task
uncertainty. Environmental uncertainty arises due to general environmental conditions
underlying the inter-organizational business relations; partnership uncertainty emerges from one
firm’s perceived uncertainty about its specific partner’s behavior; and task uncertainty arises
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from specific set of tasks carried out in the inter-organizational interactions. In this study we
only use the first two categories to assess the information processing needs of a firm. Task
uncertainty was not included for two reasons. First, the level of our analysis is at the product
level, and since there are multiple activities between two business partners for a single product it
is difficult to select a specific task for analysis. Furthermore, selection of one specific task may
not provide a comprehensive assessment of uncertainty in that relationship. Second, our initial
reservations were reinforced by the weak results reported in Bensaou and Venkatraman’s (1995)
study where two of the three task uncertainty variables were found to be not significantly
different between the different clusters of inter-organizational relationships.
Environmental Uncertainty: Researchers in organizational theory have identified two major
dimensions for uncertainty, complexity and dynamism (Duncan, 1972; Miller and Friesen,
1982). While complexity captures the number of factors and their interactions relevant to
decision making, dynamism captures the relative rate of change to those factors and ability to
predict those changes. In this study we use product complexity to represent the first dimension,
and technology uncertainty, demand uncertainty, and supply uncertainty to capture the
dimension of dynamism. We use the term uncertainty in all these variables, rather than
dynamism, to be consistent with the terminology used in prior studies. In the procurement
context the changes in technology, product demand and supply are the major factors that
influence the information processing needs (Heidi and John, 1990; Walker and Weber, 1984;
Bensaou and Venkatraman, 1996; Bensaou and Anderson, 1999). We include a fifth variable,
product criticality, to reflect the importance of the uncertainty (captured in the product
dimensions) on the information processing needs.
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In TCE research Malone et al. (1987) identified product description complexity as a factor
contributing to uncertainty in the transaction and thereby the governance structure. A complex
product without standard specifications would require more interactions with trading partners
and more information to clearly specify/comprehend the requirements. A customized product
adds to the complexity and therefore the information requirements. It requires extensive search
to find the right supplier, significant coordination between the partners, and joint action in the
design and manufacturing process (Monteverde and Teece, 1982). Firms prefer tight integration
between the trading partners to facilitate easy information flow and reduce transaction cost.
Empirical studies have found support for moving from market to long-term relationship or in-
house manufacture as the product description complexity increases (Cannon and Perreault, 1999;
Masten, 1996). Hence, product description complexity has a significant influence on the type
and amount of information to be shared and thus the technology used for communication
between trading partners.
Technology uncertainty or (un) predictability has been extensively studied in many
marketing studies and found to have a significant impact on procurement activity, particularly on
the governance structure for the transaction (Walker and Weber, 1984; Heidi and John, 1990). It
refers to the inability to forecast accurately the technical or design requirements for the product.
Frequent improvements in product functionalities, manufacturing process innovation, and
changes in design of the component create the need for significant information communication
needs between the trading partners. Richer communication channels such as face-to-face
communication and internet-based communication that are capable of communicating both
structured and unstructured communication are more likely to be preferred in such contexts.
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Demand uncertainty reflects the changes in demand for the product and the inability to
accurately predict these fluctuations (Walker and Weber, 1984). Buyers require more frequent
information communication and tighter information linkages with their suppliers to reduce the
demand uncertainty. Since the primary strategy for reducing demand uncertainty is the timely
availability of relevant information, systems that provide near real-time information to trading
partners will be preferred. Typically, most information that is shared will be structured
information of demand and delivery schedules.
Supply uncertainty represents the dynamism in the supply market that the buyer has to be
encounter while trying to match the firm’s product needs with procurement strategies. An
important contributor to uncertainty in procurement is the volatility of the supplier market in
terms of availability/supplies, stability of the suppliers, and prices. Significant supply market
dynamism can generate uncertainty related to price, availability, quality and service stability,
thereby leading to procurement risk for the buyer (Cannon and Perreault, 1999). Under such
conditions firms prefer more information for decision-making.
Product criticality is an important factor that influences the extent of monitoring of the
product and therefore the information processing needs. It is likely that the firm will share
information more extensively with the suppliers of a critical product to minimize stock-outs that
could lead to stoppage of operations. In a manufacturing firm, direct materials that are required
for daily production are more likely to be closely monitored with daily or hourly updates from
suppliers compared to some MRO items. Cannon and Perreault (1999) found it to be an
important variable to influence information exchange and the relationship between trading
partners.
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Partnership Uncertainty: A significant source of uncertainty for a firm is the uncertainty in the
relationship with its trading partners, which can further aggravate environmental uncertainty.
TCE research identifies this uncertainty in terms of two types of transaction risk, moral hazard
and adverse selection. A firm faces the possibility of the partner under-performing on its
contract or being locked up in a relationship with relationship-specific investments that do not
have value outside that relationship. Bensaou and Venkatraman (1995) identified three variables
to capture the uncertainty in the partnership. They are mutual trust, firm’s asset specificity or
relationship-specific investment, and partner’s asset specificity. These three variables capture the
two risk dimensions that create the uncertainty in the relationship. We also use a similar
conceptualization in our research model.
Relationship-specific investments by the firm and the supplier provide a strong signal to the
other partner about their desire for long-term relationships (Ganesan, 1994). Tight inter-
organizational relationship requires significant information exchange to support that relationship
(Premkumar and Ramamurthy, 1995). Researchers examining sociological factors identified
trust as a very important variable that impacts inter-organizational relationships (Hart and
Saunders, 1998). Trust refers to confidence that the behavior of the other party conforms to
one’s own expectations. Trust in the supplier reduces the perception of risk associated with
opportunistic behavior (Ganesan, 1994). Greater trust in the trading partner will result in greater
information sharing between the two firms.
Information Processing Capabilities
Consistent with the study’s objective of understanding inter-organizational systems usage, the
information processing capabilities is conceptualized as the level of technology use in various
activities of the procurement life cycle. In this regard we differ from Bensaou and Venkatraman
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(1995) study, since we are examining IT usage at a more granular level of analysis and
categorizing firms based on their IT capabilities and usage. Conceptualizing various inter-
organizational interactions using the procurement life cycle and examining IT usage in each
activity is a unique contribution of this study. Most studies in the past have examined the use of
a single technology (e.g., EDI) at the firm level. Developing organizational categories based on
the range of technology use provides a more accurate classification of firms and is a better
reflection of their IOS usage.
Communications Technologies: Exchanges between buyers and sellers and therefore the
technologies used can be classified into three broad categories – one-to-one, one-to-many, and
many-to-many (Kaplan and Sawhney, 2000). One-to-one, or dyadic, interactions were the
predominant mode of interaction for many years. Most were non-computer based
communication methods including face-to-face, paper, telephone, and fax technologies.
Subsequently, EDI replaced the non-computer communications with direct communication
between computers without manual intervention. EDI brought significant benefits due to
automation of the communication of structured information. It was not a radical transformation
in communication structure, but essentially a conversion of non-computer processes to computer-
based processes. However, the transition to EDI was not easy since documents had to be
standardized across industries and electronic capability made available on both ends of the dyad.
Private exchanges have been the first attempts to change the structure of communication
from a dyadic to a one-to-many interaction, leveraging the potential of Internet to provide web-
based access to many suppliers using a single interface. Most firms develop a web-based
customizable interface that allows qualified suppliers to access a variety of information on their
supplies such as purchase orders, current order status, demand forecasts, delivery schedules,
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inventory status, shipping, vendor ratings etc. They may have additional facilities for email,
chat, and other real-time coordination capabilities. Typically, these exchanges are well suited for
existing suppliers, but do not provide any support in the search and negotiation phase for
identifying new suppliers, or evaluating and negotiating with different suppliers. Some large
firms with sufficient visibility and procurement volume have attempted to post their RFP/RFQ
on their private exchanges. However, since the technology is a “pull” technology more often
only the existing suppliers respond to these requests, making the search process not very
effective.
Public E-markets further exploit the potential of modern communications technology for
collaboration by creating many-to-many interfaces that transform the communication structure
between buyers and sellers. These many-to-many connections can be facilitated through an
independent intermediary or an industry owned consortium. This approach provides maximum
exposure to both buyers and sellers. In theory, the large number of participants provides liquidity
to sellers, and choice and price competition to buyers. However, the large number of unknown
participants raises the uncertainty and transaction risk. Further, facilities for deep collaboration
in all phases of procurement life cycle (PLC) typically do not exist in these public E-markets.
Most of them are designed for search and fast price negotiation through auction mechanisms,
rather than for order coordination and management. Although a few E-markets provide
rudimentary facilities, most public E-markets have to be supplemented with independent
electronic dyadic links for order coordination and management activities. Industry consortium
based E-markets have attempted to address these issues. They strive to strike a balance between
private and public E-markets by allowing the selection of a preferred supplier list to reduce the
uncertainty and transaction risk, and better facilities for order coordination. The buyer-based
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industry consortiums raise concerns among suppliers as they worry about these consortiums
becoming cartels for buyers that will squeeze them of all profits. The markets are in a flux with
experimentation on various options including some hybrid ones. In our study we consider four
technology categories - non-computer, EDI, private exchanges and public E-markets.
Procurement Life Cycle: Although procurement often is treated as a single activity, it consists
of multiple activities, as exemplified in the stages of procurement life cycle (Leenders et al.,
2001). Typically, the life cycle identifies five stages – search, negotiation and pricing, ordering,
order coordination, and payment. The first stage involves developing the product specifications
and searching for potential suppliers; the second stage includes evaluation of suppliers and
negotiation of prices; the order stage is the formal contracting process with the issue of a
purchase order; the order coordination or management stage involves coordination with the
trading partner for delivery scheduling and shipping; and the last stage involves payment and
completion of the order. Each stage has different communication needs. While some stages may
have unstructured communication that is difficult to automate such as communicating
customized product specifications or price negotiations, other stages involve straightforward,
structured transaction communication such as orders, shipments, invoices, etc.
Prior research highlights the need for a good fit between the task and the technology that is
used to complete the task. Technology, due to hype or novelty, may find some initial acceptance,
but very soon may face discontinuance if it does not help users in their task. While telephone or
face-to-face communication may (still) be appropriate for many tasks, EDI may be appropriate in
other stages for structured transaction communication, a web-based system may be useful for
providing easy access to real time information, and an auction system in an E-marketplace may
be ideal for price discovery in certain contexts. The communication technology to support the
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various stages vary based on the requirements of the activity; firms may therefore use different
technologies for different sub-activities, or, alternatively, technologies may evolve to provide a
broad range of easy-to-use communication capabilities.
Research from practice suggests that firms choose a portfolio of technologies to suit their
various procurement needs. For example, Starr et al. (2001) illustrate the procurement portfolio
of Dow Chemical Co., which clearly highlights the role of product transaction characteristics in
determining the appropriate technology. They use six different options:
(a) One-on-one – set of direct connections for fulfillment of strategic materials
(b) E-mart – internal buying tool with multi-vendor catalog for non-production goods
(c) Elemica – consortium e-market for fulfillment of production materials
(d) Trade-Ranger – consortium e-market for sourcing, pricing, and fulfillment of non-production goods
(e) ChemConnect – independent e-market for commodity raw materials
(f) SciQuest – independent e-market for laboratory scientific supplies
Starr et al., (2001) further suggest that independent e-markets are better suited for low risk
trading such as purchasing MRO and non-production goods, finding new trading partners, and
spot trading on commodities. Private e-markets and one-on-one interactions are preferred for
purchasing critical production materials that are complex and require deep collaboration with
proprietary data sharing. Although industry consortia may develop the capabilities for deep
collaboration, firms are likely to be reluctant to use these facilities due to competitive
compulsions, and the consortia may run the risk of losing transaction liquidity and thereby be of
limited value to its participants.
Research Methodology
Data Collection
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Since this study examines the complex interaction of management practices and technology
based on a comprehensive evaluation of procurement activities at the product level, data was
collected through face-to-face interaction with persons responsible for sourcing and procuring
products. To facilitate the gathering of detailed data, we augmented individual interviews with a
standardized survey instrument (making use of measurement scales that are well-grounded in
theory). Hence, a semi-structured interview process combined with a survey instrument was
used to collect both qualitative and quantitative information. The survey instrument measures
the variables using multiple indicator items derived from validated instruments used in prior
studies. The interview process provides information on the organizational context, which
sometimes may not be adequately reflected in a survey instrument. We believe the interviews
helped us to understand the procurement decision-making process, allowed us to more deeply
explore survey responses, and better inform our data analysis.
Sample
Since product characteristics have a key role in influencing the transactional environment and
therefore supply chain integration, we focused on manufacturing industries as this provided us
with a wide variety of transactional environments. Initially, one researcher briefed the
participants on the study objectives and the data collection method. Each participant was
requested to select two different products from the various products that they were responsible
for buying. After completing the survey, they participated in an open discussion with the
researchers that lasted for 1-2 hours. Obtaining qualitative and quantitative information from
multiple respondents in a firm provided rich information on their supply chain, product
characteristics, supplier relationships, procurement philosophies, and their IT infrastructure. We
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collected data on procurement practices for 142 products from 84 buyers/procurement managers.
The sample characteristics are provided in Table 1.
<<< Insert Table 1 about here >>>
The participating firms were in different manufacturing industries and were generally large firms
with sales revenue ranging from $300 Million to 17 Billion. Most participants were senior
buyers or purchase managers with significant procurement experience. The products included
both direct materials (67%) and indirect materials (33%).
Measurement
All constructs assessing information needs were measured with multi-item indicators. All these
indicators were appropriately phrased as statements and measured with a Likert type scale on a
1-7 range (1= strongly disagree; 7 = strongly agree). Information technology capability was
assessed on use of one four technologies (non-computer, EDI, private market, and public E-
marketplace) on eleven procurement activities that are representative of the five stages of
procurement life cycle. Extensive psychometric testing of the constructs ensured the validity and
reliability of the constructs. For the sake of brevity, the results of these tests are not reported in
this paper. Table 2 provides the basic information on all the constructs. Appendix 1 lists the
measurement indicators for various constructs.
<<< Insert Table 2 about here >>>
Results: Cluster Analysis
Since we are using the concept of ‘fit’ to identify the information processing needs and
capabilities, cluster analysis was chosen as the most appropriate statistical technique (Hair et al.,
1991). Cluster analysis classifies objects so that each object is very similar to others in the
cluster, thereby exhibiting high internal (within-cluster) homogeneity and high external
21
(between-cluster) heterogeneity (Hair et al., 1991). The value of cluster analysis lies in the pre-
classification of data, as suggested by “natural” groupings of the data themselves. There are three
major stages in the analysis – partitioning, interpretation, and validation. The partitioning stage
is the process of determining how the clusters may be developed, the interpretation stage is the
process of understanding the characteristics of the cluster, and the validation stage involves
assessing the validity and generalizability of the clusters. As recommended by Hair et al., (1991)
each of these stages was rigorously followed, as described below.
Partitioning
The partitioning stage requires making decisions on various parameters including: (a) the
identification of variables for grouping, (b) the algorithm to be used for placing similar objects
into clusters, (c) the method to measure inter-object similarity, and (d) the number of clusters to
be generated. Since there are a number of options for each parameter and the results would vary
based on the options chosen, it is very important to carefully evaluate the problem context and
the data characteristics to decide on the options. A brief description of the options and the
criteria for selection is provided below.
The variables selected for grouping should be based on prior research that provides the
theoretical foundations for defining the generated clusters. Although the objective of cluster
analysis is to understand the natural grouping of items, the entire exercise becomes a theoretical
if we are unable to understand and explain the groupings. Hence, prior theory should drive the
selection of variables. The second decision involves choosing between two broad categories of
clustering algorithm: hierarchical and non-hierarchical or k-means cluster. In the hierarchical
method each case starts out as a separate cluster and is aggregated together into new
agglomerative clusters in each subsequent step based on some criteria. The non-hierarchical
22
method starts with a cluster center or seed and all objects within a pre-specified threshold
distance are included in the resulting cluster. It typically requires pre-specification of the
number of clusters and the seed value for the variables in each of the clusters. Both methods have
their advantages and disadvantages. Although, in the past, hierarchical method was extensively
used, in recent years the non-hierarchical method has become popular. This method is less
susceptible to outliers in the data, inclusion of inappropriate variables, and the distance measure.
However, an important issue is the selection of appropriate seeds for the initial cluster based on
theoretical or practical considerations. Researchers recommend combining the two procedures to
gain the benefits of both methods (Hair et al., 1991). First, the hierarchical method is used to
establish the number of clusters and the cluster centroids. Then, the cluster centers from that
analysis are used as seeds in the non-hierarchical method. There are different methods within the
hierarchical method including single linkage, complete linkage, average linkage, centroid, and
Ward’s method. Typically, Ward’s method, where the within-cluster sum of squares is
minimized over all partitions in each stage, is preferred.
The third decision relates to determining the method to measure inter-object similarity.
Some of the common methods to measure distance are absolute or city block distance,
normalized distance, Euclidean distance, and Mahanalobis distance. Typically, the scales used
for measurement and the correlation among the variables determine the appropriate similarity
measure, but the last two methods are preferred for most common situations. The fourth
decision of choosing the number of clusters post hoc is a more difficult decision, unless there are
strong reasons to justify the number of clusters. Prior research on cluster analysis has evaluated
various procedures for determining the number of clusters (Milligan and Cooper, 1984; Calinski
and Harabasz, 1974). Hair et al., (1991) suggest that the decision on the number of clusters
23
should be driven by theoretical and intuitive considerations. They recommend the use of the
information generated by the agglomeration schedule in the hierarchical method. The
agglomeration coefficient, a measure of distance between two clusters (or cases) being combined
at each stage in the cluster analysis, is a useful indicator to determine the similarity of clusters.
A small coefficient value indicates that two similar clusters (or cases) are being combined. A
large increase in the value of agglomeration coefficient suggests the merger of dissimilar clusters
and therefore a good cut-off point. This analysis is similar to the scree plot in factor analysis,
which is used to determine the optimal number of factors.
All variables in our study were identified based on theoretical considerations that were
described in the earlier section discussing the research model. We used a two-step approach to
determine the clusters. First, the hierarchical method was employed to determine the number of
clusters and cluster centroids, and then the non-hierarchical (k-means) clustering algorithm was
used with the centroids from the earlier analysis as the initial cluster seeds to identify the
membership in the clusters. Based on recommendations from prior researchers (Punj and
Stewart, 1983; Hair et al., 1991; Milligan and Cooper, 1985) we used: (a) standardized values for
each variable, (b) squared Euclidean distance for similarity assessment, and (c) Ward’s minimum
variance method for cluster formation.
Table 3a and Table 3b show the largest percentage changes in agglomeration coefficient
from the agglomeration schedule of the hierarchical method for the cluster analysis, respectively,
of information needs and information capabilities.
<<< Insert Table 3a & 3b about here >>>
The results show a large increase when going from a one-cluster to two-cluster solution and
subsequent changes are smaller. Based on all these information a 2-cluster solution was found to
24
be appropriate in both the cases. In the second step, as noted earlier, the centroids from the 2-
cluster solution were used as seeds for K-means clustering algorithm. The final cluster centers
based on the K-means cluster analysis are shown in Tables 4a & 4b.
<<< Insert Table 4a & 4b about here >>>
We noticed that that the membership of data points in the clusters created by the non-hierarchical
method was not very different from the hierarchical method, thereby confirming the stability of
the cluster analysis.
Interpretation
Interpretation involves evaluating the theoretical rationale for forming the clusters, examining
the centroids of the variables in the clusters, and developing a label that uniquely describes each
cluster. The interpretation of the two-cluster analysis, one for information needs and another for
information capabilities, is separately discussed below. Based on prior research we determined
that information needs is based on various product and supplier-relationship characteristics that
lead to uncertainty in the transactions. We posited that higher levels of product complexity,
demand uncertainty, technology uncertainty, supply-market uncertainty, product criticality,
investments by trading partners in each other, and trust would generate greater information
needs. Table 4a provides the centroid values for all the variables in the 2 clusters, and Figure 2
illustrates graphically the variation between the two clusters. Besides the mean, the univariate F
values and significance levels are provided for each variable.
<<< Insert Figure 2 about here >>>
Only 2 of the 8 variables, demand uncertainty and trust, are not significantly different between
the two clusters. The mean values for all the variables in cluster-1 are greater than cluster-2.
Products in cluster-1 compared to cluster-2 exhibit higher complexity, greater technology
25
uncertainty, more product criticality, higher supply market uncertainty, and greater investment in
the relationship by both partners. Based on the theoretical rationale presented earlier we expect
that products in cluster-1 would have higher information needs and therefore resort to more
collaborative relationship with its partner compared to cluster-2 where, due to their reduced
information needs, we expect a more autonomous relationship. Hence, the two clusters are
labeled as collaborative and autonomous relationship to describe the information needs and
transaction relationships.
The second cluster analysis, using the same sequence of development noted earlier, created
two clusters of information capabilities based on technology use in various activities in the
procurement life cycle. Table 4b provides the centroid values for the variables in the 2-cluster
solution and Figure 3 illustrates the same information in a graph.
<<< Insert figure 3 about here >>>
All the variables are significantly higher in cluster-2 compared to cluster-1. Except the last
activity, payment, all the other variables were significant at p < 0.01. The value of technology
use in payment indicates that firms still prefer either the paper check or the EDI/EFT method for
payment, but cluster-2 prefers EDI/EFT payment more than cluster-1. An examination of the
values for all the other variables indicates cluster-2 has significantly greater electronic support
compared to cluster-1. Also, cluster-2 uses web more in the search phase and a combination of
Web and EDI-type technologies for structured communication such as purchase order, delivery
schedule, shipping etc., in the later phases. Hence, we label cluster-1 and cluster-2 as low
electronic support and high electronic support, respectively.
26
Validation
The third stage of cluster analysis, namely validation, ensures that the cluster solution developed
is stable and generalizable across the population. One approach would be to run cluster analysis
with separate samples or sub-groups of samples and compare the results. When sample size
constraint exists multiple cluster analysis with the same sample but with different starting
centroids is suggested as an alternative (Hair et al., 1991). We chose the second option and
performed two separate cluster analysis for the two contexts. The results indicate that the
solutions are stable with almost the same number of data points in each cluster for the two cluster
analyses. The significant gap in the mean values of the variables between the two clusters for
both the contexts, shown in Table 4a & 4b, also provides strong support for the stability of the
membership in the clusters.
Another important aspect of validation in cluster analysis is profiling, where the
characteristics of the cluster are profiled using well-understood variables, representative of the
label, not included in the cluster analysis. It can be considered as an assessment of the predictive
validity of the clusters. An analysis comparing the values of a variable that is expected to differ
between the clusters is used for validation. Since the first cluster analysis created two clusters
that is based on the relationship between the trading partners (collaborative and autonomous) we
used an independent single-item measure assessing the level of strategic business relationship
between the partners to determine if it was significantly different between the two clusters. The
mean values and the significant difference in the values between the two groups (collaborative =
5.03, autonomous = 3.75, t=5.19, p<0.001) indicate that the collaborative cluster has a higher
level of business relationship compared to autonomous cluster, providing validity to our clusters.
The second cluster analysis identified two clusters, high and low electronic support. Since one
27
could logically argue that high electronic support group would have a higher level of electronic
information exchange with their partner, a single item independent measure of level of electronic
information exchange with trading partner was compared between the two clusters. It was found
to be significantly greater for high electronic support group (High = 4.85, Low = 4.27, t=1.88, p
< 0.06), also attesting the validity of our clusters.
Results: Information Processing Fit
Our research model posits that the “fit” between information processing needs and information
processing capabilities have an impact on performance. As described earlier, we use the “fit as
matching” perspective to assess the fit between the two variables. Fit is operationalized as the
interaction of the two variables, which is captured by the interaction effects in ANOVA.
Therefore, we expect the interaction effects to have a greater impact on performance compared
to the main effects (Venkatraman, 1989). The results of ANOVA are provided in Table 5.
<<< Insert Table 5 about here >>>
The individual measures of performance along various dimensions as well as the aggregated
measure of performance are shown. The mean sums of squares and the significance of the main
and interaction effects for each of the performance variables are provided. The results indicate
that while the interaction effect of the two variables has a significant effect on the overall
performance, the main effects are not significant. This supports the notion of fit that posits that
the fit or interaction between the two variables rather than the variables themselves is a predictor
of performance.
Among the individual performance measures the interaction effect had a significant influence
on three of the six measures, which further confirms the notion of fit between information
28
processing needs and capabilities. Interestingly, in each of these three instances the main effects
were not found to be significant.
Discussion
We developed a research model based on information processing theory and the concept of fit to
understand the relationship between information processing needs created by the product and
relationship environment characteristics and information processing capabilities in the supply
chain, and then evaluate the impact of the fit between the two constructs on performance. The
results of ANOVA indicate that the interaction effects are more significant than the main effects,
lending validity to the concept of fit and our initial proposition that it is not the individual
variables but the fit between the two variables that results in better performance. The success of
E-markets for MRO items but not in direct production goods is a real-world validation of the
same concept (Kaplan and Sawhney, 2000; Starr et al., 2001). MRO type items, which are more
likely to be standardized (less complex), less critical, and with lower demand and supply
uncertainty, are more suited to E-markets. Most firms would be reluctant to risk procuring direct
material that affects their daily production and final product quality from new suppliers in an E-
market. Prequalification of suppliers, mutual trust, and long term collaboration are the typical
norms for procuring critical products. A dyadic interaction is normally the preferred method for
such procurement.
We used the “fit as gestalt” concept to develop a cluster of products with similar relationship
characteristics that require comparable information processing needs. These two clusters match
with the transaction cost economics perspective of governance structure, namely, markets and
hierarchies, since the autonomous cluster favors loose relationship as in markets and the
collaborative cluster favors tight integrative relationship reflected in hierarchies. The 2-cluster
29
solution is consistent with prior results that used similar variables for clustering (Bensaou and
Venkatraman, 1995). Although statistically a 2-cluster solution was suggested, we did examine
clusters beyond the two to identify intermediate or hybrid forms relationship that have been
suggested in recent research. The 3-cluster and 4 cluster solutions, although providing some
meaningful grouping, were not very stable and more difficult to interpret. Perhaps, a larger
sample size and a greater variety in transaction environments beyond the manufacturing industry
may provide more information on hybrid relationships.
The results of cluster analysis found that only 2 of the 8 variables used in cluster analysis,
demand uncertainty and trust, were not significantly different between autonomous and
collaborative clusters. The mean value of demand uncertainty was not significantly different
between the two clusters, but the values were in the right direction. There could be two reasons
for this result. Prior theory has found contradictory results on this variable. While some
researchers have argued that greater demand uncertainty leads firms to form tight collaborative
relationships with their suppliers so that they could order and get products at short notice
(Bensaou and Venkatraman, 1995; Buvik and John 2000), other researchers have suggested that
markets provide greater flexibility to a firm since it has greater supply capacity and therefore is
capable of supplying at short notice (Bensaou and Anderson, 1999). The results, perhaps, reflect
both the procurement strategies used to address demand uncertainty. Trust was another variable
that was not significant. An examination of the mean and standard deviation for this variable
revealed that there was very little variation among the data points, suggesting that, either most of
the participating firms in this study had high trust in their trading partner or the measurement did
not adequately tap into the construct. Since the measure was previously validated we expect it to
30
be the former reason. More research is required to determine the reason for lack of difference in
trust between autonomous and collaborative clusters.
The cluster analysis of information processing capabilities also revealed a two-cluster
solution that clearly differentiated the electronic support between the two clusters. Figure 2
shows some interesting patterns. There is a greater tendency to use the Internet in the search
phase as seen by the higher mean values in the first two activities. The high electronic support
cluster leverages the Internet and perhaps electronic markets for posting RFI/RFP and price
negotiation while the other, low electronic support cluster, still uses the traditional non-computer
interaction for these two activities. Typically the activities in the search phase are more
unstructured compared to other activities. While the gap between the two clusters is high in the
first four activities it reduces for activities numbered 5 to 11, which represent ordering and post-
order coordination and payment. This suggests that firms are more amenable to using electronic
technologies such as EDI for communicating structured documents. It also suggests an evolution
in the technology with some of the initial technologies such as EDI being more prevalent
compared to the web and E-markets, which are relatively newer and capable of supporting semi-
structured and unstructured communication.
A more detailed assessment of the usage of technologies in these activities is provided in
Figures 4a to 4e for the purposes of illustration.
<<< Insert Figures 4a to 4e about here >>>
For the sake of simplicity we have chosen one representative activity from each stage of the
procurement life cycle – search, negotiation/price discovery, ordering, order coordination, and
payment. The bar charts show the proportion of users using the technology for each group. The
charts provide a graphical illustration of fit within each category with different uses of
31
technologies in different procurement activities, as well as fit between information processing
needs (collaborative and autonomous) and capabilities as reflected in the differences in the use of
technology for the two clusters. Within the PLC we see web technologies used more extensively
in the search phase. The web, perhaps not surprisingly, provides an excellent opportunity for the
buyer to search a large number of suppliers. The development of more sophisticated search
engines has made the task easier. The web is also an attractive tool for suppliers to disseminate
information on their products. In fact the web is becoming a large global E-market, at least in
the search phase, attracting both buyers and sellers. The low cost of entry with minimal
downside risk, combined with a simple technology interface, motivates firms to try the
technology in the search phase. However, this technology is of less value for products in the
collaborative cluster since the suppliers are more likely to be already identified. Also, since the
products are more likely to be complex and customized requiring direct interactions with those
suppliers the web is of less value as it is more suited for searching for standardized products,
similar to those found in the autonomous cluster. Pricing and negotiation involve unstructured
communication, except in the context of highly standardized products or commodities, and
therefore non-computer (face to face) communication is the most favored option. We do see an
increase in use of Web for this phase. Auction systems, popularized by E-markets have not
caught on very much, except in a few products in the autonomous category. EDI is most popular
in order and order management activities as it involves communication of fairly standardized
information. Firms still prefer non-computer communication (paper check) for payment.
Conclusions
The availability of sophisticated information systems and a ubiquitous network linking most
businesses has created new opportunities for electronic integration between firms. However,
32
companies need to be cognizant of the requirements of business processes in their supply chain
and inter-organizational relationships when choosing from among a wide range of
communication technologies and implementation strategies. This study used Galbraith's (1973)
information processing theory to examine the fit between information processing needs and
information processing capabilities in an inter-organizational supply chain context and examine
its impact on performance. Taxonomies of information processing needs based on inter-
organizational transaction characteristics, and information processing capabilities based on IT
use in various activities of the procurement life cycle are developed. The study collected data on
142 products through personal interviews and surveys primarily from firms in the manufacturing
industry. Cluster analysis was used to develop the taxonomies for information processing needs
and information processing capabilities. The results revealed two clusters each in information
processing needs and information processing capabilities. The fit between the two variables was
examined using ANOVA, and the results showed that the interactive effect of needs and
capabilities had a greater effect on performance than the main effects, supporting our fit theory.
The mean values of the variables were examined in the two clusters to better understand the
characteristics of the two clusters.
This study makes both theoretical and methodological contributions to IS research. From a
theoretical perspective, formulating and empirically testing the information processing theory’s
concept of fit between the information processing needs and capabilities is a unique contribution
to IS research since the notion of ‘fit’ has not been empirically examined extensively.
Developing taxonomies of information processing needs and information processing capabilities
using a rigorous approach to cluster analysis is a methodological contribution of this research.
The study also identified some exciting ideas for future research. Out taxonomy of information
33
needs generated a two-cluster solution, which while consistent with a prior study, did not seem to
capture the wide range of hybrid relationships that are discussed in practice. This could be
perhaps an artifact of our sampling, which focused only on manufacturing industry. A more
comprehensive sampling of different industries may provide a broader range of inter-
organizational relationships. Another avenue for future research would be to expand the range of
activities to include strategic activities such joint product development, marketing promotion etc.
In-depth examination of these partnerships could reveal the evolution from information sharing
to knowledge management that significantly enhances the value of the partnership and the most
appropriate types of information technology support to sustain such evolution and relationships.
There are some limitations that need to be recognized. The data for the study were primarily
collected from manufacturing firms in the mid-west region and that limits the generalizability of
the results.
34
References
1. Bakos, J.Y. “Reducing Buyer Search Costs: Implications for Electronic Marketplace,” Management Science (43:12), 1997, pp. 1676-1692.
2. Balakrishnan, S.B., and Wernerfelt, B. “Technical Change, Competition, and Vertical Integration,” Strategic Management Journal (7:2), 1986, pp. 347-359.
3. Bensaou, M., and Anderson, E. “Buyer-Supplier Relations in Industrial Markets: When Do Buyers Risk Making idiosyncratic Investments,” Organization Science (10:4), 1999, pp. 460-481.
4. Bensaou, M., and Venkatraman, N. “Inter-Organizational Relationships and Information Technology: A Conceptual Synthesis and a Research Framework,” European Journal of Information Systems (5:1), 1996, pp. 84-91.
5. Bensaou, M., and Venkatraman, N. “Configurations of Inter-organizational Relationships: A Comparison between US and Japanese Automakers,” Management Science (41:9), 1995, pp. 1471-92.
6. Benson, J.K. “The Interorganizational Network as Political Economy,” Administrative Science Quarterly (20:June), 1975, pp. 229-249.
7. Buvik, A., and John, G. “When Does Vertical Coordination Improve Industrial Purchasing Relationships?” Journal of Marketing (64:October), 2000, pp. 52-64.
8. Calinski, J. and Harbasz, J., “ A Dendrite Method for Cluster Analysis,” Communications in Statistics, 3, 1974, pp. 1-27.
9. Cannon, J.P., and Perreault, W.D. “Buyer-Seller Relationships in Business Markets,” Journal of Marketing Research (36:November), 1999, pp. 439-460.
10. Chandler, A.D., Strategy and Structure, 1962, MIT Press, Cambridge, MA.
11. Choudhary, V., "Strategic Choices in the Development of Interorganizational Information Systems," Information Systems Research, 8(1), 1997, pp. 1-23.
12. Chwelos, P., Benbasat, I., and Dexter, A.S., “Empirical Test of an EDI Adoption Model,” Information Systems Research (12:3), 2001, pp. 304-321.
13. Daft, R.I. and Lengel, R.H., Information Richness: A New Approach to Managerial Behavior and Organization Design. In B.M. Straw and L.L. Cummings (Eds), Research in Organizational Behavior, Vol. 6, 1984, pp. 191-233, JAI Press, Greenwich, CT.
14. Daft, R.I. and Lengel, R.H., “Organizational Information Requirements, Media Richness, and Structural Design,” Management Science, 32, 1986, pp. 554-571.
15. Duncan, R.B., “Characteristics of Organizational Environments and Perceived Environmental Uncertainty,” Administrative Science Quarterly, 1972,
16. Dwyer, R.F., Schurr, P.H., and Oh, S. “Developing Buyer-Seller Relationships,” Journal of Marketing (51:April), 1987, pp. 11-27.
17. Emerson, R.M. “Power-Dependence Relations,” American Sociological Review (27:Feb.), 1962, pp. 31-41.
35
18. Galbraith, J.R., and Nathanson, D., “The Role of Organizational Structure and Process in Strategy Implementation,” in D. Schendel and C.W. Hofer (Eds), Strategic Management: A New View of Business Policy and Planning, 1979, pp. 249-283, Little Brown, Boston, MA.
19. Galbraith, J.R. Designing Complex Organizations. Addison-Wesley, Reading, MA, 1973.
20. Ganesan, S. “Determinants of Long-term Orientation in Buyer-Seller Relationships,” Journal of Marketing (58:April), 1994, pp. 1-19.
21. Hair, J.F, Jr., Anderson, R.E., Tatham, R.L., Black, W.C. Multivariate Data Analysis with Readings, 1991, Macmillan Publishing Co., New York, NY.
22. Hambrick, D.C., “Taxonomic Approach to Studying Strategy: Some Conceptual and Methodological Issues,” Journal of Management, 1984, 10, pp. 27-42.
23. Handfield, R.B., and Nichols, E.L.Jr., Supply Chain Redesign - Transforming Supply Chains into Integrated Value Systems, 2002, Prentice Hall, Upper Saddle River, NJ.
24. Hart, P., and Saunders, C.S. “Power and Trust: Critical Factors in the Adoption and Use of Electronic Data Interchange,” Organization Science (8:1), 1997, pp. 23-42.
25. Hart, P., and Saunders, C.S. “Emerging Electronic Partnerships: Antecedents and Dimensions of EDI Use from Supplier’s Perspective,” Journal of Management Information Systems (14:4), 1998, pp. 87-111.
26. Heidi, J.B., and John, G. “Alliances in Industrial Purchasing: The Determinants of Joint Action in Buyer-Supplier Relationships,” Journal of Marketing Research (27:10), 1990, pp. 24-36.
27. Jain, H., Ramamurthy, K., Ryu, H.S., and Yasai-Ardekani, M. “Success of Data Resource Management in Distributed Environments: An Empirical Investigation,” MIS Quarterly (22:1), 1998, pp. 1-23.
28. Kaplan, S., and Sawhney, M. “E-Hubs: The New B2B Marketplaces,” Harvard Business Review (78:3), May/June 2000, pp. 97-103.
29. Leenders, M., H. Fearon, A. Flynn, P. Johnson, Purchasing and Supply Management, 12th edition, 2002, McGraw Hill-Irwin, NY, New York.
30. Malone, T.W., Yates, J., and Benjamin, R. “Electronic Markets and Electronic Hierarchies,” Communications of the ACM (30:6), 1987, pp. 484-497.
31. Masten, S.E. “Empirical Research in Transaction Cost Economics: Challenges, Progress and Directions,” in Transaction Cost Economics, J. Groenewegen (eds.), Kluwer Academic Publishers, Boston, 1996, pp. 43-64.
32. Miles, R.E. and Snow, C.C., Organizational Strategy, Structure, and Process, 1978, McGraw Hill, New York, NY.
33. Miller, D and Friesen, P.H, “Innovation in Conservative and Entrepreneurial Firms: Two Models of Strategic Momentum,” Strategic Management Journal, 3, 1982, pp. 1-25.
34. Milligan, G.W. and Cooper, M.C., “An Examination of Procedures for Determining the Number of Clusters in a Data Set,” Psychometrika, 50, 1985, pp. 159-179.
36
35. Monteverde, K. and Teece, J.D. “Supplier Switching Costs and Vertical Integration in Automobile Industry,” Bell Journal of Economics (13:1), 1982, pp. 206-213.
36. Premkumar, G., and Ramamurthy, K. “The Role of Inter-organizational and Organizational Factors on the Decision Mode for Adoption of Inter-organizational Systems,” Decision Sciences (26:3), 1995, pp. 303-36.
37. Punj, G., and Stewart, D., “Cluster Analysis in Marketing Research: Review and Suggestions for Application,” Journal of Marketing Research, Vol. 20, 1983, pp. 134-148.
38. Reve, T. and Stern, L.W. “Interorganizational Relations in Marketing Channels,” Academy of Management Review (4:3), 1979, pp. 405-416.
39. Sabherwal, Rajiv, and Yolande E. Chan. “Alignment Between Business and IS Strategies: A Study of Prospectors, Analyzers, and Defenders.” Information Systems Research 12, no. 1 2001, pp. 11-33.
40. Starr, E.C., Kambil, A., Whitaker, J.D., and Brooks, J.D. “ One Size Does Not Fit All – The Need for an E-Marketplace Portfolio,” Advances in Supply Chain Management, Vol. III, 2000, pp. 96-99.
41. Stern, L.W. and Reve,T. “Distribution Channels as Political Frameworks: A Framework for Comparative Analysis,” Journal of Marketing (44:Summer), 1980, pp. 52-64.
42. Stern, L.W. and El-Ansary, A.I. Marketing Channels, Prentice Hall, Englewood Cliffs, NJ, 1982).
43. Thompson, J.D. Organizations in Action, McGraw Hill, New York, 1967.
44. Tushman, M.L. and Nadler, D.A. “Information Processing as an Integrating Concept in Organizational Design,” Academy of Management Review (3:July), 1978, pp. 613-624.
45. Venkatraman, N., “ The Concept of Fit in Strategy Research: Towards Verbal and Statistical Correspondence,” Academy of Management Review, 14(3), 1989, pp. 423-444.
46. Walker, G., and Weber, D. “Supplier Competition, Uncertainty, and Make-or-Buy Decisions,” Academy of Management Journal (30:3), 1984, pp. 589-596.
47. Williamson, O.E. “Comparative Economic Organizations: The Analysis of Discrete Structural Alternatives,” Administrative Science Quarterly, 36, 1991, pp. 269-296.
48. Williamson, O.E. Markets and Hierarchies: Analysis and Anti-trust Implications, The Free Press, New York, 1975.
49. Zapf, M. “ISM/Forrester Research Report on e-Business – July 2002,” http://www.forrester.com.
37
Table 1 – Sample Characteristics
Variable Frequency in %
Sales Revenue (millions) Less than or equal to 250 251 - 500 501 - 1000 1001 - 2500 2501 - 6000 Greater than 6000 Job Title Vice President Director Manager Senior Buyer Buyer Products Direct Production Goods Indirect Materials
10.0% 10.0% 20.0% 20.0% 20.0% 20.0%
1.2% 6.0% 63.1% 27.4% 2.4%
66.9% 33.1%
Table 2 – Descriptive Statistics
Variable Mean Std. Dev.
Reliability Alpha
Description Complexity 4.42 1.74 0.84
Technology Uncertainty 3.25 1.40 0.76
Demand Uncertainty 4.10 1.41 0.67
Product Criticality 5.75 1.61 0.88
Supply Uncertainty 3.90 1.18 0.76
Firm Investment 3.35 1.63 0.82
Supplier Investment 4.22 1.57 0.81
Trust 5.80 0.77 0.87
38
Table 3a – Agglomeration Schedule – Information Needs
Number of Clusters Percentage Change in Agglomeration
Coefficient
2 26.6
3 16.0
4 10.0
5 7.8
6 8.5
Table 3b – Agglomeration Schedule – Information Capabilities
Number of Clusters Percentage Change in Agglomeration
Coefficient
2 21
3 10
4 10
5 10
6 7
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Table 4a – Cluster Analysis – Information Processing Needs
Collaborative Mean (N=86)
AutonomousMean (N=57)
Mean Square
F Sig.
1. Description Complexity 5.58 2.67 289.29 293.73 0.000
2. Technology Uncertainty 3.64 2.67 31.80 18.16 0.000
3. Demand Uncertainty 4.15 4.03 0.53 0.27 0.606
4. Product Criticality 6.43 4.72 100.80 53.35 0.000
5. Supply Uncertainty 4.10 3.60 8.40 6.21 0.014
6. Firm Investment 4.32 1.88 203.22 163.96 0.000
7. Supplier Investment 5.06 2.96 151.60 108.13 0.000
8. Trust 5.86 5.70 0.90 1.51 0.221
40
Table 4b – Cluster Analysis – Information Processing Capabilities
Low Electronic Support (N=64)
High Electronic Support (N=55)
Mean Square
F Sig.
1. Search 2.17 3.04 22.11 38.58 0.000
2. Catalog 2.11 2.69 10.00 11.71 0.001
3. RFI/RFP 1.45 2.78 52.22 96.61 0.000
4. Pricing 1.30 2.38 34.82 47.18 0.000
5. Purchase Order 1.86 2.33 6.48 10.26 0.002
6. Engineering Document 1.80 2.58 18.23 26.74 0.000
7. Demand Forecast 1.59 2.35 16.71 29.69 0.000
8. Delivery Schedule 1.72 2.27 9.08 14.78 0.000
9. Inventory 1.50 2.25 16.84 27.20 0.000
10. Shipping/ Logistics 1.72 2.42 14.47 28.07 0.000
11. Payment 1.56 1.84 2.22 3.64 0.059
41
Table 5 – ANOVA: Effect of Fit of Information Needs and Information Capabilities on Procurement Performance
Information Needs Information Capabilities Needs X Capabilities Variable
Mean Sq. Sig. Mean Sq. Sig. Mean Sq. Sig.
Performance 0.403 0.531 0.554 0.463 3.319 0.074
Order Cost 4.737 0.143 0.013 0.937 3.472 0.210
Cycle Time 0.094 0.819 0.020 0.915 9.470 0.023
Inventory Turnover 6.070 0.063 0.659 0.537 3.271 0.170
Product Price 6.182 0.061 2.780 0.207 1.138 0.419
Supplier Coordination 0.295 0.653 2.021 0.241 6.854 0.032
Information for Decision Making 3.410 0.155 0.288 0.678 6.916 0.044
Figure 1 – Research Model
Relationship Uncertainty • Firm-investment • Supplier-investment • Trust
Environmental Uncertainty
• Product Description Complexity • Technology Uncertainty • Demand Uncertainty • Supply Uncertainty • Product Criticality
Information Processing
Needs
Information Processing
Capabilities
Fit Performance
IT Support for Procurement Life Cycle Activities • Non-Computer Support • EDI • Private E-marketplace • Public E-marketplace
42
43
Figure 2 – Cluster Analysis - Information Processing Needs
Figure 3 – Cluster Analysis – Information Processing Capabilities
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Figures 4a–4e: Technology Use
Figure 4a - Search Figure 4b – Pricing
Figure 4c – Order Figure 4d – Delivery Schedule
Figure 4e - Payment
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Collaborative Autonomous
Non-ComputerEDIInternetE-market
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010203040506070
Collaborative Autonomous
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Appendix-1 Measurement Indicators
All items measured using a 7-point Likert type scale varying from strongly disagree to strongly agree. Complexity 1. Amount of technical and other product information that needs to be provided for the supplier to
understand our needs 2. Familiarity of product’s specifications to most suppliers 3. Extensive customization of the product to meet our requirements Technology Uncertainty 1. Frequent occurrence of major product innovations in the product 2. Frequent changes to the product’s design/functionality 3. Frequent changes made by the supplier to the product’s specifications Demand Uncertainty 1. There is significant uncertainty in our demand for this product 2. Demand forecasts in terms of volume and timing are not accurate Product Criticality 1. Stock out of this product will create major disruptions to operations 2. This product is critical to our operations 3. This product’s quality has a significant impact on the performance of the end product Supply Uncertainty 1. There is significant price variation among suppliers of this product 2. There is considerable variation in quality/service among suppliers 3. Over time, the price of this product fluctuates widely 4. Over time, the availability of this product fluctuates widely Firm-Investment 1. Our firm has provided tooling, warehouses, or other special equipment/facilities to this product supplier 2. Our firm has modified product design to tailor it to supplier needs 3. Our firm has made a major investment in time and effort to develop/ learn business practices to meet
supplier's needs Supplier-Investment 1. This supplier has reserved a portion of its manufacturing/distribution capacity to meet our demand 2. This supplier has invested in information systems, tooling, warehouses, or other special
equipment/facilities to meet our needs 3. This supplier has recruited people with specific manufacturing/design skills & expertise to meet our
requirements Trust 1. We believe in honesty & accuracy of deadlines set by this supplier 2. This supplier delivers on the promises made to us 3. This supplier is honest in all business dealings 4. This supplier is willing to share information with us 5. This supplier honors all agreements with us 6. This supplier exhibits consistency in business dealings with us