Safeguarding Investments in Asymmetric Interorganizational
Relationships: Theory and Evidence
MANI R. SUBRAMANI 3-358 Carlson School of Management, University of Minnesota
321, 19th Ave S., Minneapolis, MN 55455 Tel: (612) 624-3522, Fax: (612) 626-1316
email: [email protected]
and
N. VENKATRAMAN David J. McGrath Jr. Professor of Management
School of Management, Boston University 595, Commonwealth Avenue
Boston, MA 02215 Tel: (617)-353-7117, Fax: (617)-353-5003
email: [email protected]
Forthcoming in Academy of Management Journal
This material is based on work supported by the National Science Foundation under grant number 9808042 to the first author and SBR-9422284 to the second author. The research project was also supported by the Systems Research Center at Boston University. We thank. John Henderson, Gurbux Singh, and managers at the retailer organization for their support and assistance at different stages of this project. We also thank George John and Akbar Zaheer for insightful comments on earlier versions of the paper. Further, the comments of the Guest Editor – Rita McGrath and three anonymous AMJ reviewers were extremely useful in developing the ideas presented here.
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Safeguarding Investments in Asymmetric Interorganizational Relationships: Theory and Evidence
Abstract
We model the governance strategies adopted by suppliers to safeguard relationship-
specific investments in asymmetric interorganizational relationships using two dimensions—
quasi integration and joint decision making. Data from a field study of 211 supplier
relationships in a distribution channel support the research model. Domain knowledge
specificity arising from relationship-specific intellectual capital investments emerges as the
most influential determinant of governance. The results provide preliminary but powerful
evidence of the value of intangible assets in interorganizational relationships.
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Vertical interorganizational relationships in organizational networks are often
characterized by considerable power asymmetries, and supplier firms are vulnerable to the
exercise of power by the more powerful firm. Achieving a greater understanding of the
linkage between relationship-specific supplier investments and the nature of safeguards
established to protect them is therefore an important issue for supplier firms and their more
powerful partners, both of whom seek benefits from their cooperative vertical relationships.
In this paper, we examine how vulnerable suppliers, who typically do not have the
bargaining power to extract safeguards for their investments in the relationship ex ante, craft
governance mechanisms that have the effect of safeguarding them ex post.
From the theoretical perspective of transaction cost economics, cooperative
relationships between firms reflect the increased use of nonmarket governance. Parties in
such relationships have overlapping roles, engage in considerable coordinated action, make
bilaterally negotiated changes to the terms of the exchange on an ongoing basis, and rely on
internal enforcement by establishing a mutuality of interest between parties. Interfirm roles
can become so closely intertwined that the firms’ boundaries approach complete
interpenetration (Rindfleisch & Heide, 1997). Governance mechanisms are means to provide
safeguards for asset specificity arising from relationship-specific investments that are only
partially redeployable and therefore are valuable only in the context of the exchange (Stump
& Heide, 1996). Prior research has identified a variety of governance mechanisms that
provide safeguards for such specialized assets, to protect the firm making relationship-
specific investments from opportunistic behavior by its partner (Rindfleisch & Heide, 1997).
These include formal contracts (Joskow, 1988), pledges (Anderson & Weitz, 1992),
information sharing (Noordwier, John & Nevin, 1990), supplier verification (Heide & John,
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1990), joint planning (Heide & John, 1990), monitoring (Stump & Heide, 1996), and quasi
integration (Zaheer & Venkatraman, 1994).
Despite the important insights into vertical interoganizational relationships provided by
prior research (Masten, 1984, Stump & Heide, 1996; Walker 1994), the literature is
incomplete in several respects.
First, much of the prior work adopts the perspective of dominant focal firms such as
large automakers (Walker & Weber 1984) or large utilities (Joskow 1988) that have the
bargaining power to extract safeguards for specific investments in interorganizational
relationships. Requiring safeguards for vulnerable relationship specific assets is akin to the
extraction of hostages to preclude opportunistic behavior (Stump & Heide 1996).
In contrast, asymmetric relationships in which parties make relationship-specific
investments but do not have the ability to require safeguards for them consistent with the
expectations of theory are understudied in the literature. For instance, how does a medium-
sized manufacturing firm extract safeguards for the significant investments in unique,
relationship-specific, just-in-time processes it must make if it is to work with a large retailer?
While dependence-balancing investments (Heide & John, 1988) such as brand building to
create customer demand for the manufacturer’s product are suggested by prior research,
only a small fraction of manufacturers have the resources to adopt this course. The
literature’s emphasis on the perspective of powerful firms in making governance choices has
unfortunately overlooked the predicament of weaker partners.
Second, while there is considerable recognition that interorganizational relationships are
an important means of leveraging intangible assets in supplier relationships (Dyer & Singh
1988), little attention has been paid to how intangible-asset specificity influences suppliers'
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governance choices. In vertical relationships, relationship-specific intangible assets can have
many forms: investments in standard operating procedures created and refined over multiple
cycles of action in the exchange, skills created through specific training, learning-by-doing
and particularistic experience, and new relationship-specific organizational knowledge
created in the context of the exchange. While relation-specific intangible assets have been
viewed as components of asset specificity, prior conceptualizations have associated these
investments largely with the individuals involved in the exchange and considered them to be
manifested as human-capital asset specificity (Masten 1984, 1988). We increasingly have
evidence that even though individuals are involved in action, significant components of
intangible assets are strongly associated with organizations and embedded in the multiple
roles that form the fabric of the firm's operating processes rather than in particular
individuals (Montverde, 1995; Simon, 1996). Rindfleisch and Heide describe these intangible
assets as "a substantial and important cost of doing business" (Rindfleisch & Heide, 1997:
41). Disaggregating the broad construct of intangible asset specificity into sub-constructs and
examining their influences on governance mechanisms is therefore an important and critical
extension to theory and research. Further, this would provide empirical support for the
argument that intangible relationship-specific assets, rather than tangible ones increasingly
form the basis for contracting in value chains (Dyer & Singh, 1988).
We address these issues in this paper. In particular, we focus on governance
mechanisms crafted by smaller, peripheral firms in their interaction with dominant buyers in
the context of a distribution channel—in asymmetric relationships between suppliers of
goods and a large retailer. We suggest that suppliers, who are usually unable to require
safeguards for their vulnerable relationship specific investments ex-ante, evolve them ex-post
through two governance mechanisms: quasi integration with the dominant buyer and
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participation in joint decision-making. Quasi integration makes it possible for the supplier
firm to focus on delivering value to the dominant buyer, effectively ensuring the
continuation of the relationship in subsequent periods. Joint decision making provides
suppliers the means to influence the dominant buyer’s key decisions in a manner that serves
the suppliers’ own interests and lets supplier firms safeguard their relationship-specific
investments. Moreover, these safeguards for relationship-specific assets are created in a
manner that adds value, consistent with the arguments of the transaction value perspective
(Zajac & Olsen, 1993; Dyer, 1997).
GOVERNANCE IN VERTICAL INTERORGANIZATIONAL RELATIONSHIPS
Recent refinements and interpretations of transaction cost economics (Heide, 1994;
Williamson, 1995) view cooperative interfirm relationships as reflecting a shift away from
arm’s-length, market-based exchanges towards closer, cooperative, nonmarket governance.
The hand-in-glove buyer-supplier relationships through which firms leverage resources in
the supplier network and manage ongoing accommodations to the exchange represent
instances of this governance form. Within non-market governance structures, we examine
mechanisms crafted by supplier firms in organizational networks in their dealings with
dominant buyers.
Prior studies in a variety of contexts (Dyer & Singh, 1998; Heide & John, 1990) highlight
two important features of such relationships. First, supplier firms focus closely on the needs
of the dominant retailer in exchanges in order to be able to deliver value. Second, the parties
in the relationship engage in cooperative, bilateral negotiations and integrative problem
solving. Consistent with these observations, we conceptualize the nonmarket governance
mechanisms created by supplier firms as comprising two dimensions. The structural
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dimension- quasi integration, reflects the extent to which the activities of the supplier firm
are linked to the retailer. The process dimension- joint decision making, reflects the
supplier’s level of participation in decision-making in its relationship with the retailer.
Quasi Integration We define the level of Quasi Integration as the degree of linkage between the supplier and the
dominant buyer in the relationship. A supplier's choice either to deal at arm’s length with a
variety of buyers or to work closely with a small number of them is a critical decision with
significant consequences; it largely determines the degree of linkage between the activities of
the supplier and buyers. A supplier’s choice to allocate a significant proportion of its output
to one particular buyer reflects the supplier's strategy of working closely with that firm; it is a
de-facto choice of integration or quasi integration with the specific buyer (Blois, 1972). With
greater reliance on nonmarket governance in recent years, researchers focused on this
dimension of integration. For example Christiaanse (1994) views the percentage of a travel
agent's annual ticket bookings accounted for by a given airline as reflecting the degree of
quasi integration between the agent and the airline. In a similar vein, Zaheer and
Venkatraman (1994) view the proportion of an insurance agent's revenues accounted for by
a particular insurance carrier as reflecting the degree of quasi integration of the agent's
activities with the insurance company.
Increasing levels of quasi integration represent a departure from the arms-length
relationships of spot markets as the identity of the buying firm assumes greater importance
to the supplier. With higher levels of quasi integration, there is considerable communication
and information exchange in the relationship between the supplier and the dominant buyer,
and the resources of the supplier are increasingly oriented towards serving the changing
needs of the buying firm in a distinctive way (Dyer, 1996; Zajac & Olsen, 1993).
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A high level of quasi integration by a supplier is a credible commitment to the dominant
buyer as it reflects the supplier firm’s decision to forego alternatives and rely on the
relationship to achieve a large proportion of its revenue goals (Anderson & Weitz, 1992).
Prior research indicates that explicit signals by one firm conveying the likelihood of
trustworthy behavior to the partner, particularly in contexts in which monitoring is likely to
be imperfect, trigger reciprocal commitments by the other party in the relationship. Explicit
signals by one party thus set up cycles of commitment escalation that successively bind the
parties into a long-term relationship (Anderson & Weitz, 1992; Dyer, 1996). Consistent with
this view, we suggest that a high level of quasi integration signals strong intentions of
trustworthy behavior with respect to the dominant buyer by the supplier, and is important in
moving the supplier and the retailer towards a closer, more integrated relationship, which in
turn, safeguards the supplier’s relationship-specific investments.
This type of integration is at the heart of the fundamental transformation that
Williamson describes as “the transformation of what had been a large numbers bidding
competition at the outset into one of bilateral exchange during contract execution and at
contract renewal intervals" (Williamson, 1995: 230). In the context of distribution channel
relationships between suppliers and a dominant retailer, greater quasi integration ensures that
supplier efforts are concentrated on continually meeting the requirements of the retailer. The
supplier’s high degree of focus on the retailer provides significant value to the retailer, which
the retailer would lose if it were to switch suppliers. Therefore, higher levels of quasi
integration, by increasing switching costs, help safeguard the supplier’s relationship-specific
investments (Anderson & Weitz, 1992). Effectively, a high level of quasi integration and the
attendant focus can make the supplier a preferred choice among the set of suppliers
competing for the retailer’s business in the next ordering cycle. Overall, our arguments
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suggest that quasi integration not only serves as a mechanism to safeguard relationship-
specific investments but also does so in a manner that maximizes the overall transaction
value. Whereas the establishment of safeguards is usually viewed as creating transaction
costs (Stump & Heide, 1996), quasi integration in fact contributes to transaction value, as
suggested by Zajac & Olsen (1993) and Dyer (1997).
Joint Decision Making We define the level of joint decision-making as the degree to which the supplier firm and the
dominant buyer jointly make decisions with respect to key issues in the relationship. Firms depart from
the rigid demarcation of roles characteristic of market governance to the sharing of roles and
responsibilities across organizational boundaries when they engage in joint decision making.
Joint decision making enhances the degree of participative management of the
interorganizational relationship (Heide & John, 1990) and is a central component of
cooperative strategies in dyads (Dyer & Singh, 1998).
In cooperative supplier-retailer relationships in the distribution channel, participation in
joint decision making lets suppliers play an active role in shaping the retailer's decisions
regarding their products. In contrast, in market exchanges (where arm’s-length relationships
between suppliers and retailer prevail), the retailer is likely to decide independently to carry a
certain quantity of a specific product at a particular price and then call for multiple bids from
suppliers (Stern & Ansari, 1988). When a supplier participates in the joint decision-making
process, the firm can influence the retailer’s decisions in a manner that safeguards their own
interests (Milgrom & Roberts, 1986). Joint decision making consequently serves the
important function of safeguarding suppliers’ relationship-specific asset investments.
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DETERMINANTS OF GOVERNANCE
Role of Intangible Assets Intangible investments have consistently emerged as significant determinants of
governance in multiple contexts (see Rindfleisch & Heide, 1997, for a summary).
Investments in intangible assets are recognized as embedded in organizational routines
(Nelson & Winter, 1982), knowledge processes (Nonaka, 1994) and core competencies
(Hamel & Prahalad, 1996). However, in the literature drawing on transaction cost
economics, intangible assets have largely been conceptualized and operationalized as being
embedded within individuals and giving rise to human-capital specificity (Masten 1984,
Masten 1988; Monteverde, 1995). This view of intangible assets is limiting, as we increasingly
have evidence that intangible assets are strongly associated with organizations and embedded
in the multiple roles that form the fabric of a firm's operating processes and the knowledge
they draw on (Simon, 1996; von Hippel, 1994; Zack, 1999).
Kogut and Zander (1992) view intangible assets in organizations as comprising two
components: know-how and know-what. Know-how refers to the firm-level understanding
of task execution linked to the associated intangible investments that are made to conceive
tasks and create standard operating procedures for efficient task execution. The other
component of intangible resources, know-what, refers to context-sensitive, tacit
understanding of subtleties that allows effective action and the resolving of ambiguities in
task planning and execution.
Drawing on this distinction, we suggest that relationship-specific intangible investments
in the buyer-supplier context can be analogously conceptualized in terms of these two
dimensions. This would allow us to distinguish between the qualitatively different
investments in establishing and refining standard business processes for interacting with the
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dominant buyer from those in developing domain expertise related to the buyer and the
context of the exchange. We term the relationship-specificity of these two components as
business process specificity and domain knowledge specificity, respectively.
Business Process Specificity. We define the extent of business process specificity as the
degree to which critical business processes of one firm are specific to the requirements of the other firm in an
interorganizational relationship. Specialized business processes include context specific processes
for new product introduction, customer service, inventory management, and quality control.
Specialized routines or standard operating procedures evolve over time in organizations
through the codification and institutionalization of successful patterns derived from repeated
execution of activities (Nelson & Winter, 1982).
Specialized routines created to enact a particular interorganizational exchange generally
have little value outside the relationship. For instance, specialized production and
manufacturing processes created by components suppliers in the automobile industry to
implement Just-in-Time (JIT) deliveries for specific customers need to be completely
redesigned if the suppliers desire to make JIT deliveries to another automobile assembler
(Klier, 1993). In implementing JIT delivery of products to automobile assemblers, suppliers
make significant changes to their own materials procurement, manufacturing scheduling, and
logistics processes. These changes are designed to provide them the capability to deliver
precise lot sizes (determined by the assembler’s production plan) at very short and precise
intervals before the components are required on the assembly line. JIT supply generates
significant cost savings by eliminating the costs of carrying and managing component
inventories throughout the system, and is achieved by the supplier customizing a wide range
of its processes for the specific auto assembler (Klier, 1993). Clearly, the intangible
investments made by suppliers are highly specialized to suit specific customers, are of limited
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value in other exchanges, and reflect high levels of business process specificity. Other
examples of customization that leads to business process specificity include insurance agents’
creation of administrative procedures that are specific to particular insurance carriers (Zaheer
& Venkatraman, 1994) and automobile component manufacturers’ development of
customer-specific engineering and manufacturing processes to work with large automakers
(Bensaou & Venkatraman, 1995).
We expect a higher level of business process specificity in an exchange to be related to a
higher level of quasi integration. Relationship-specific investments represent assets that
deliver superior value in the context of the relationship than in alternative contexts. We
therefore expect greater business process specificity to indicate greater levels of supplier
interest in working with the retailer, as greater business process specificity makes it possible
for supplier firms to differentiate themselves advantageously in the relationship. In turn, this
is likely to be reflected in a greater share of the output supplied to the retailer, enhancing the
level of quasi integration.
In addition, the higher the level of business process specificity, the greater the supplier’s
motivation for joint decision making, as joint decision making makes it possible for the
supplier to influence the retailer’s decisions in a manner that is favorable to the firm
(Milgrom & Roberts, 1986). Further, participation in decision making allows suppliers to
identify opportunities to improve their deployment of relationship-specific business
processes (Dyer & Singh, 1998). This enhancement in value delivery increases the likelihood
of the exchange being continued in the future, effectively safeguarding the supplier’s
investments in relationship-specific business processes.
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Overall, these arguments, both from the perspective of safeguarding specialized assets
and of enhancement of value delivered in the exchange, suggest that higher levels of
business process specificity are related to higher levels of nonmarket governance in the form
of quasi integration and joint decision making. We therefore propose the following
hypothesis:
Hypothesis 1. In asymmetric cooperative vertical relationships, the level of business process specificity is positively related to the level of quasi integration and joint decision making
Domain Knowledge Specificity. We define the extent of domain knowledge specificity as
the degree to which critical areas of knowledge of the supplier firm are specific to the
requirements of the buyer in an interorganizational relationship. Domain knowledge
specificity refers to an organization’s ability to access and deploy a specific body of prior
knowledge (Nonaka, 1994; Teece, 1998) in the interorganizational relationship. Important
domains of organizational expertise in the retail distribution channel that are specific to a
particular relationship include competitive analysis, strategy formulation, and new product
conception. Specialized knowledge is created through social processes that encourage the
validation, refinement, and enrichment of knowledge in the context of action (Nonaka,
1994). Prior research in a variety of contexts suggests that such specialized knowledge tends
to be domain specific with imperfect transferability across contexts (Shanteau, 1992).
The customization of knowledge to a specific domain occurs when organizational
resources are applied to understanding patterns and rules particular to a specific context.
Expertise deployment leads to increasingly effective issue diagnosis and problem solving
based on greater levels of familiarity and understanding of the nuances of a particular
exchange. While such domain specific knowledge is very valuable in the context of the
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relationship, investments made in creating the knowledge have lesser value in other
alternative relationships.
Domain knowledge specificity is also traceable to social factors unique to the context of
the exchange. The development and refinement of knowledge in a specific social context
leads to the creation of expertise that is sticky and less amenable to application and transfer
to other contexts (von Hippel, 1994). This is often manifested in context-specific judgments
in which some events are deemed meaningful and needing attention while others are
considered irrelevant and ignored. These judgments occurring in a socially defined context
are often distributed among multiple members involved in the situation. This is particularly
so in supplier-retailer relationships, in which the constituent expertise is distributed among
multiple individuals in the firms involved. In such instances, the knowledge required for
coordinating the application and deployment of expertise is context specific as well. Because
the expertise of both firms in the interorganizational exchange is complementary, the
expertise as a whole is sited in the specific context and only partially redeployable.
In practice, domain knowledge specificity arises in interfirm contexts both from the
uniqueness of the expertise and from the distribution of the expertise among the key
personnel of the interacting firms. For instance, we interviewed managers at a manufacturing
firm, working closely with a specific retailer to develop new features in snow-blowers and
lawnmowers for the retailer’s customers – a feature-conscious market segment. The
supplier’s managers indicated that their knowledge and understanding would be of limited
use in other domains; it would be only partially applicable in dealings with other retailers e.g.
those catering to price sensitive market segments and requiring different product attributes.
The supplier’s managers also recognized that their knowledge could not be directly deployed
elsewhere because they relied on key components of complementary information and
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knowledge possessed by the retailer’s merchandisers. Uzzi (1997) observed similar patterns
of specialized expertise embedded in the context of interfirm relationships in the garment
industry.
We expect higher levels of domain knowledge specificity in an exchange to be related to
a higher level of quasi integration. From the perspective of the supplier, greater investments
in domain-specific knowledge would be associated with higher levels of commitment to its
interorganizational partner because these assets would have more value in the context of that
relationship than they would in other contexts. This is likely to be reflected in a greater
share of output being supplied to the focal firm, enhancing the level of quasi integration.
This is consistent with the higher levels of safeguards that quasi integration provides for
investments in specialized domain knowledge.
We also expect higher levels of domain knowledge specificity to be related to higher
levels of joint decision making. Participation in joint decision-making involves the pooling of
information by participants (Heide & John 1990, Zaheer & Venkatraman, 1994). Joint
decision-making thus allows suppliers to anticipate and influence decisions in ways favorable
to their own interests. Thus, higher levels of investments in the relationship in the form of
domain knowledge specificity would enhance the value suppliers would contribute by
engaging in joint decision-making. Further, participation in decision-making allows suppliers
to identify opportunities and influence actions in a manner that improves the outcomes of
the deployment of their expertise in the exchange (Dyer & Singh, 1998). This enhancement
in value delivery increases the likelihood of the exchange being continued in future periods,
effectively safeguarding investments in domain knowledge that would be diminished in value
if the exchange were discontinued. Overall, these arguments, both from the perspective of
safeguarding specialized assets and of enhancement of value delivered in the exchange,
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suggest that higher levels of domain knowledge specificity are related to higher levels of non-
market governance. We therefore propose the following hypothesis:
Hypothesis 2. In asymmetric vertical cooperative relationships, the level of domain knowledge specificity is positively related to the level of quasi integration and joint decision making.
Role of Tangible Assets
In vertical relationships, suppliers often make relationship-specific investments in the
form of tangible assets such as plant and machinery and in location choices that are
advantageous in working with a specific buyer (Williamson, 1995). For instance, a garment
supplier we interviewed indicated that they had invested in a tunnel-ironing machine
specifically for a retailer that ordered garments to be shipped directly to retail stores hung in
wardrobe boxes so that they could be directly transferred to racks on the floor. The
machine was not used in supplying other retailers whom the manufacturer dealt with, as they
ordered garments to be delivered boxed, which required that the standard steam press be
used. This is an instance of an investment in a relationship-specific physical asset. The
location of manufacturing plants and warehouses also reflects the relationship specific
investments made by suppliers in vertical relationships (Dyer, 1994). Suppliers can choose to
locate their manufacturing plants at locations that make them particularly advantageous in
supplying a particular focal firm or locate them in a manner that they are equally useful in
supplying multiple retailers. In the former case, there is a high level of site specificity.
Together, physical-asset specificity and site specificity capture the significant dimensions of
tangible-asset specificity in supply relationships in the distribution channel. Tangible-asset
specificity operates in much the same manner as intangible-asset specificity. We therefore
hypothesize that:
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Hypothesis 3. In asymmetric vertical cooperative relationships, the level of physical-asset specificity is positively related to the level of quasi integration and joint decision making. Hypothesis 4. In asymmetric vertical cooperative relationships, the level of site specificity is positively related to the level of quasi integration and joint decision making.
Control Variables
Clearly, any model with a set of focused relationships requires that rival hypotheses be
discounted. We therefore incorporated five variables that are recognized as having an
influence on governance choice: relational flexibility, size of the supplier, length of
association, dependence on retailer and uncertainty.
Relational Flexibility. We define relational flexibility as the bilateral expectation that
changes will be made to the commercial working relationship to redress hardship when a
party is adversely affected by changing circumstances in the exchange. This definition of
flexibility in the relationship reflects the expectation that good-faith adjustments will be
made if specific contractual obligations or stipulations become unviable or cumbersome due
to unanticipated contingencies. Conversely, this definition incorporates the trustful belief
that one party does not take advantage of the other when unexpected changes make one of
them vulnerable to opportunistic exploitation by the other (Heide, 1994). This construct is a
focused operationalization of the broader definitions of trust in the context of buyer-
supplier relationships.
Greater levels of relational flexibility reduce the inherent risks of making challenging
commitments, thereby expanding the arena of collaborative action (Ring & Van de Ven,
1992). Increasing levels of relational flexibility encourage greater sharing of information and
greater exploration of opportunities to maximize joint outcomes (Dyer, 1996). The level of
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relational flexibility is therefore likely to influence the level of nonmarket governance in
interorganizational relationships.
Size. We include the size of the firm as a variable in our model to control for
extraneous factors such as relative bargaining power and a small resource base, factors that
may influence the governance of the exchange. Larger suppliers have the resources to make
investments in branding that reduce their dependence on the retailer; they may be more
successful in directly extracting hostages than smaller firms and thus be less dependent on
bilateral governance mechanisms to protect their vulnerable assets.
Length of Association. It is quite likely that supplier firms in close cooperative
relationships with high levels of quasi integration and joint decision making will have, over
time, greater opportunity to develop specialized assets in the exchange. For instance, it is
likely that a supplier’s participation in joint decision making creates a context for learning
that leads to greater levels of domain knowledge specificity in the next period. Including the
length of association as an independent variable controls for this recursive relationship, in
the model1.
Supplier Dependence. Resource dependence theories (Pfeffer & Salancik, 1978)
suggest that the extent to which a supplier is dependent on a specific retailer influences the
character of interorganizational relationships and is thus likely to be influential in
determining the nature of governance mechanisms as well. For example, if a supplier relies
heavily on a retailer’s supplier-assistance services, this is likely to influence the supplier’s
governance choices in the relationship. We therefore include dependence in the model to
1 We are grateful to a reviewer for this insight.
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examine the influence of asset specificity on governance over and above that attributable to
the level of supplier dependence in the exchange.
Uncertainty. The level of uncertainty in the exchange is recognized in prior research as
a factor that influences the nature of governance mechanisms (Rindfleisch & Heide, 1997;
Williamson, 1995). In buyer-supplier relationships, uncertainty arises both from changes to
the product and from changes in the environment of the exchange. Higher levels of
uncertainty demand greater adaptation of the terms of the exchange, and in the process,
expose the supplier’s relationship-specific assets to the possibility of opportunistic behavior
by the retailer. This is likely to influence the nature of governance mechanisms selected. We
therefore include uncertainty in the model to control for the influence of this variable.
Our research model is represented in Figure 1. All notations in the figure follow the
standard conventions of structural equation modeling (Joreskog & Sorbom, 1993). We posit
a bidirectional link between the two dimensions of governance (ψ21).
Insert Figure 1 about here
METHODS
Research Context
The distribution channel for consumer products in Canada served as the setting for the
study. The distribution channel comprises a complex chain of organizations that interact to
supply products and services to customers (Stern & Ansari, 1988). The choice of
distribution is a complex issue that has significant implications for supplier firms’ market
positioning and for their internal operations (Heide, 1994). In 1996, the retail market in
Canada comprised six major Canadian retailers: Sears, Zellers, The Hudson's Bay Company,
Eaton's, Kmart, and Walmart. This group of retailers accounted for over 80 percent of the
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retail merchandise sales in the country. Our interviews indicated that supplier-retailer
relationships were asymmetric with the retailers in general being in a stronger position.
Suppliers indicated that they routinely accepted orders from retailers governed by ‘back-of-
form’ contract terms with almost no tailoring of these terms to their particular requirements.
However, some leading retailers were engaged in redesigning their operations and
procurement processes to involve suppliers in activities such as forecasting and ordering
(Chain Store Age, 1995).
This study was facilitated by the cooperation of a large, well-established Canadian retailer
that we refer to as RetCo. Sales from their 110 stores comprised about 20 percent of retail
sales in Canada. As part of the initial phase of the fieldwork, one of the authors attended 8
day-long sessions RetCo conducted with selected suppliers. In the second phase of
fieldwork, we conducted hour-long semi-structured interviews with 27 managers involved in
various roles drawn from both sides of six selected supplier-RetCo relationships.
We collected field data through a survey of RetCo’s suppliers, on their relationship with
RetCo. The sampling frame was the set of over 2000 firms listed in the retailer's supplier
database. Suppliers who provided less that 0.5 percent of a department's purchases in a
calendar year were largely firms who had either supplied samples or had made ad-hoc, one-
time supplies and not considered active suppliers. Excluding those firms, we were left with a
sample of 640 regular suppliers with whom the retailer had ongoing supply relationships.
Over 90 percent of the retailer's purchases in the prior year were made from this set of
suppliers.
By focusing our data collection on the supplier network of one large retailer, we reduced
the range of extraneous variations that might influence the constructs of interest. In
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particular, in view of the considerable influence retailers have over the decisions of suppliers
because of the retailers’ superior bargaining power, our sampling strategy enabled us to
capture variance in suppliers’ governance choices while holding the influence of RetCo’s
supplier management strategy constant. In addition, this sampling choice made it possible
for us to supplement the data provided by suppliers with information from RetCo’s
personnel and from their supplier databases. We recognize the shortcoming of sampling a
specific subset of the population, but we believe that the advantages of this approach
outweighed the disadvantages of limited generalizability.
Measures
In most cases, measures validated in previous studies were adapted to the context. New
measures were developed for domain knowledge specificity on the lines of prior measures of
asset specificity. Business process specificity was measured using three items: level of
intangible investments in specialized accounting and inventory management processes (that
are enforced largely by the use of specialized software), investments in specialized
administrative procedures, and investments in specialized operating procedures to
coordinate with the retailer. Domain knowledge specificity was measured with three items
that reflected the level of specialized intangible investments in developing an understanding
of the retailer’s requirements and the distinct context of the interaction. The items related to
expertise developed for new product planning, product conception and design, and pricing,
three areas that interviews indicated reflected the supplier’s understanding and knowledge of
the retailer’s market positioning and customer expectations.
The level of quasi integration was measured using a single item: the percentage of the
supplier’s total annual sales made to the specific retailer, on the lines of Zaheer and
22
Venkatraman (1994) and Walker (1994). Joint decision making was measured using three
items adapted from Heide and John (1990) and Zaheer and Venkatraman (1994). Details of
the items, scales, and the sources are listed in Appendix 1.
Survey Design and Administration
The measures were field-tested and refined in multiple personal administrations of the
survey instrument to managers in supplier firms. The final survey instrument was mailed in
two waves to managers in the 640 supplier firms. Managers who did not respond within five
weeks were called to remind them to respond.
Response Rate and Nonresponse Bias
We received 211 usable responses, an effective response rate of 33 percent comparable
to that observed in prior studies in the distribution channel (Ganesan, 1994; Heide, Dutta &
Bergen, 1998).
We examined the possibility of nonresponse bias statistically as well as by calls to non-
respondents. We compared responses of early respondents and late respondents using a t
test (p < 0.10) as suggested by Armstrong and Overton (1977). This revealed no significant
differences between the two groups on items on the survey. As a further step, we compared
the set of respondents to nonrespondents using data from the RetCo’s supplier database.
We compared the groups on dollar volume of purchases in the prior year by RetCo in
different product categories and the number of purchase order infractions, a metric used to
evaluate supplier performance. We found no statistically significant differences between
respondents and nonrespondents on these factors. As an added measure, we phoned a
randomly selected set of 35 nonrespondents (5 percent of nonrespondents) to ascertain the
reasons for their not returning the questionnaire. The most common reasons given were
23
that the manager was too busy to fill out the survey or that the company’s policy was to not
respond to surveys, providing no evidence of a systematic nonresponse bias that would
affect the results.
RESULTS
Descriptive Statistics
We estimated the model using LISREL8 (Joreskog & Sorbom, 1993). This approach
allows for the simultaneous estimation of the psychometric properties of measures using the
measurement model as well as the hypothesized interconstruct relationships using the
structural model.
The sample comprises small and medium-sized firms: 59 percent of the firms have
annual sales revenues less than Can$ 20 million and 30 percent of the firms lie in the median
revenue interval between Can$11 and 20 million. Most of the informants (65 percent) are
CEOs or vice presidents with tenures of over 14 years in the firm. On average, firms in the
sample have 230 employees. The supplier firms have a long history of interaction with the
retailer, an average of over 17 years, confirming that the sample comprises firms in ongoing
mutually cooperative arrangements with the retailer2. 52 percent of the firms own brands
that were among the top three in their category, and 19 percent indicated that their brands
ranked among the top 10, providing evidence that firms in the sample are likely to have a
variety of distribution channel choices available to them. The extent to which they worked
closely with RetCo is thus likely to have been a strategic choice actively made by these firms.
The means, standard deviation, and the zero-order correlation of constructs are in Table 1.
2 The retailer did not have equity positions in any of the firms.
24
Measurement Properties
We estimated the basic measurement model for both independent and dependent
variables. The constructs displayed statistically significant item and composite reliabilities
above 0.7. We also tested for discriminant validity using standard model comparisons and
found these to be acceptable. For our governance variables, we took steps to assess the
extent to which data from the supplier survey corresponded with data from members of the
retailer’s buying group. We collected matching data from retailer managers for 165 of the
211 suppliers in our data set. The correlation between the supplier’s assessment of its quasi
integration and the buyer’s assessment of the supplier's quasi integration3 was 0.46, p < 0.01.
The correlation between the levels of joint decision-making reported by the supplier and the
retailer was 0.58, p < 0.01. The presence of significant correlations, in spite of the inherent
difference in perspectives, provides confidence in the quality of the measures for the
constructs.
As entering into quasi integration reflects a strategy by the supplier to create value in the
relationship, the level of quasi integration is likely to correspond to the level of benefits
delivered to the retailer. The degree of correlation between the level of quasi integration
reported by the supplier and the level of benefits from the relationship reported by the
retailer was 0.60, p <. 01. This significant correlation suggests nomological validity of the
measure of quasi integration.
Insert Table 1 about here
3 Correlations are between the log transformed values of the % of supplier’s sales to RetCo reported by both parties.
25
Tests of the Hypotheses
LISREL8 results of the research model Mt suggest an acceptable model specification,
reflected in the nonsignificant chi-square statistic: χ2(df: 183) = 224.34, p < 0.20. The GFI
and the AGFI are 0.89 and 0.84 respectively. The value of the Cumulative Fit Index (CFI) is
0.96 and of the NNFI is 0.97, suggesting that the research model is supported by the data.
The RMSEA, an estimate of the error of approximation, is 0.02; the 90-percent confidence
interval for RMSEA is completely within the acceptable region and lower than 0.05. Table 2
presents the model chi-square and fit estimates for the research model. Appendix-2
provides details of the parameter estimates. The findings from the tests of hypotheses
follow.
Hypothesis 1: Business process specificity is positively related to both dimensions of
governance. While the path to joint decision making is significant (γ21 = 0.14, t = 1.37, p <
0.1), the path from business process specificity to quasi integration is not significant (γ11 =
0.01, t = 0.08, ns). Hypothesis 1 is thus partially supported.
Hypothesis 2: As predicted, domain knowledge specificity is positively related to both
dimensions of governance. The path between domain knowledge specificity and quasi
integration is positive and significant (γ12 = 0.33, t = 3.40, p < 0.01) and the path to joint
decision making is positive and significant as well (γ22 = 0.23, t = 2.40, p < 0.01).
Hypothesis 2 is thus fully supported.
Hypothesis 3: Physical-asset specificity is positively related to the two dimensions of
governance. The influence of physical-asset specificity on quasi integration is small and not
26
significant (γ13 = 0.08, t = 0.58, ns) while the influence on joint decision making is positive
and significant. (γ23 = 0.28 t = 1.96, p < 0.05). Hypothesis 3 is thus partially supported.
Hypothesis 4: The influence of site specificity on quasi integration is positive but not
significant (γ14 = 0.09, t = 0.64, ns) while the effect on joint decision making is negative and
not significant (γ24 = -0.19, t = -1.34, ns). Hypothesis 4 is thus not supported.
Overall, we have significant results for four of the eight paths that we test in the model.
The results provide initial support for the role of quasi integration and joint decision making
as mechanisms of interorganizational governance, factors safeguarding relationship-specific
asset investments by vulnerable suppliers. Joint decision making emerges as more influential
in this regard than quasi integration. These results are observed after accounting for the
influence of the relationship’s social context and controlling for factors such as the size of
the supplier, the relationship history, supplier dependence, and the level of uncertainty.
Control Variables. The correlation between the control variables (Table 1) suggests
that larger suppliers in the sample have longer length of prior association (r2 = 0.18, p <
0.05). Larger suppliers are also less dependent on the retailer, viewing the retailer as more
replaceable (r2 = 0.20, p < 0.05). It is interesting that years of association is not significantly
related to replaceability (r2 = 0.02, ns), suggesting that longstanding relationships don't
necessarily create situations of supplier dependence. However, longstanding supplier
relationships are observed in contexts with higher levels of uncertainty (r2 = 0.17, p < 0.05),
providing support for the notion that relationship continuity creates a context conducive to
the development of means to manage uncertainty effectively.
The results in Table 2 suggest that relational flexibility is positively related to quasi
integration but the relationship is not significant (γ15 = 0.12, t = 1.10, ns), the relationship
27
with joint decision making is positive and significant (γ25 = 0.35, t = 3.01, p < 0.01). Size is
inversely related to the level of quasi integration (γ16 = -0.33, t = -2.28, p < 0.01) while being
positively related to the level of joint decision making (γ26= 0.20, t = 1.42, p < 0.1). These
results are consistent with the expectation that larger suppliers are likely to be less integrated
with a specific retailer than smaller suppliers. The length of prior association is inversely
related to the level of quasi integration (γ17 = -0.13, t = -1.59, p < 0.1), but not to the level of
joint decision making (γ27 = -0.04, t = -0.52, ns). Retailer replaceability is significantly and
inversely related to the level of quasi integration (γ18 = -0.29, t = -2.40, p < 0.01). The
negative sign reflects the fact that lower replaceability (higher level of dependence) is linked
to higher levels of integration, a finding consistent with theories of resource dependence
(Pfeffer & Salancik, 1978). Retailer replaceability is not, however, significantly related to the
level of joint decision making (γ28 = -0.09, t = -0.79, ns). The level of uncertainty is not
significantly related to either the level of quasi integration (γ19 = -0.03, t = -0.31, ns) or the
level of joint decision making (γ29 = 0.36, t = 0.04, ns).
Insert Table 2 about here
Comparing Plausible Alternative Models
Tests of the hypothesized model in the positivist tradition can indicate either support or
lack of support for it, but the explicit rejection of competing models strengthens the validity
of postulated relationships (Anderson & Gerbing, 1988). To this end, we developed a set of
plausible alternative models and compared them sequentially to the research model using
standard structural equation modeling procedures to examine the level of support in the data
for each. First, we tested a model that reflects the view that intangible investments can be
28
represented as one composite dimension combining business process specificity and domain
knowledge specificity. This was rejected in favor of our research model (Table 3, columns
2,3). Second, we considered an alternative model that represents a social view of exchange
relationships, such as the commitment-trust theory (Morgan & Hunt, 1994), viewing
commitment to the relationship and relationship flexibility as central to outcomes in
interfirm relationships. We represented firm investments in specialized tangible and
intangible assets as one dimension (commitment), signaling the intent to preserve and
enhance the relationship. This model was also rejected in comparison with the research
model (Table 3, columns 2,4). We also compared the research model to a third alternative
model that representing nonmarket governance as a unitary construct rather than as two
distinct dimensions. This model was also was also rejected in comparison with the research
model (Table 3, columns 2,5). These results, summarized in Table 3, further our confidence
in the research model.
Insert Table 3 about here
DISCUSSIONS AND CONCLUSIONS
Overall, our results are consistent with the theoretical predictions of transaction cost
economics, and the following results are worth highlighting. First, we found support for our
two-dimensional conceptualization of interorganizational governance comprising quasi
integration and joint decision-making. Based on a systematic test of competing models, we
found that (a) the two dimensions are different and (b) the determinants of the two
dimensions are different. Our results suggest that these two dimensions taken together are
important in ensuring that suppliers’ value-creating investments in relationship-specific
assets are safeguarded.
29
Second, we found that domain knowledge specificity arising from relationship-specific
investments in intellectual capital, rivals asset specificity from investments in physical assets
as an important determinant of governance choices. To the best of our knowledge, these
results represent the first empirical demonstration of specialized investments in intellectual
capital being more influential than those in physical assets in influencing governance
decisions. Domain knowledge specificity is a refinement of the broader concept of human-
capital asset specificity and refers to the particularistic, often experiential, knowledge created
in interorganizational settings. Just as physical asset specificity was a significant determinant
of governance in the industrial age, we believe that domain knowledge specificity has the
potential to be a key determinant of governance choices in the knowledge-driven economy.
Third, the results suggest that business process specificity is linked to joint decision
making but not to quasi integration and does not support the strong influence of this
construct reported in prior studies (e.g., Zaheer & Venkatraman, 1994). It is plausible that
this may be a consequence of our distinction between process and expertise assets as prior
researchers subsumed notions of expertise within business process specificity. It could also
be that the increasing pressures towards standardization of business processes may have
diminished the importance of business process specificity and enhanced that of domain
knowledge specificity in our context. In general, the results highlight the need for careful
attention to the dimensionalization of intangible asset-specific investments in future studies.
Contrary to our expectations, the data suggest that site specificity is inversely related to
the level of joint decision making. It is likely that site specificity in the form of the
advantageous location of plants and distribution outlets with respect to the dominant buyer
provides such overwhelming benefits in the relationship that relationship continuity is
assured, consequently reducing the need for specific governance safeguards. This is
30
consistent with the findings of Dyer (1996) of lower inventory costs for assemblers created
by site specific investments by suppliers in the auto industry.
Our study has several limitations. We collected data from suppliers to one retailer firm to
enhance internal validity and control for important retailer-specific factors, but this choice
limits the generalizability of our results. Further, the relationships we studied were
embedded within a historical context of trust and understanding: the average length of
association between suppliers and the retailer was over 17 years. Although continuation of
these relationships is dependent on seasonal or annual placement of orders, they do
nevertheless represent a sample of relatively stable relationships. The next step in researching
these issues would be to study them across multiple buying firms and in settings marked by
less continuity, such as those in business to business exchanges that are often described as
being ‘plug and play’ (Gosain, 1999).
Delving deeper into the characteristics of domain knowledge specificity arising from supplier
involvement in boundary-spanning activities like product design, product promotion, or
pricing and marketing would be interesting approaches to explore in future research. Also,
studying the governance implications of expertise embedded in the context of interfirm
interactions (Uzzi, 1997) and of expertise coordination processes in interorganizational
teams (Faraj & Sproull, 2000) would inform our understanding of important components of
intellectual capital investments by suppliers.
While we examined how vulnerable suppliers evolve governance mechanisms to
safeguard valuable assets without specific reference to the dominant partner, the moves of
dominant buyers can play an important role in creating the context for effective supplier
actions. For instance, by signaling intentions to involve suppliers in joint decision-making,
31
or by shrinking the supplier base, dominant firms can create incentives for suppliers to make
non-contractible relationship-specific commitments. The governance mechanisms we
highlight thus deserve the attention of researchers studying dominant firm strategies to
mobilize supplier investments.
Overall, this study extends our understanding of how vulnerable suppliers, who typically
do not have the bargaining power to extract safeguards for their investments ex ante, craft
governance mechanisms that have the effect of safeguarding them ex post - through quasi
integration and joint decision making. We thus present empirical evidence supporting
conceptual descriptions of the 'fundamental transformation' (Williamson, 1995) that changes
ex ante market relationships into small-numbers bargaining situations over multiple cycles of
interaction. Our results are consistent with the logic of transaction cost theory, which states
that safeguards are necessary for relationship-specific assets in exchanges because farsighted
parties would not invest in such assets otherwise. Moreover, our study contributes to the
emerging logic of the transaction value framework that suggests that governance
mechanisms can advantageously incorporate features that both enhance transaction value
and minimize transaction costs (Dyer, 1997). Further research into the nature of such
governance mechanisms is needed to develop insights that enhance our understanding of
value creation in interorganizational networks.
32
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8, �9Control Variables
�1Quasi Integration
�2Joint Decision Making
�11
�12
�21
�22
�13
�14
�24to JDM
to QI
� 21
�4
�2
�3
�1� 11
� 42
� 32
� 22
� 3
� 6
� 5
� 4
� 31
� 21
� 11 � 1 Process Specificity
� 1
� 2
� 62
� 52
� 42 � 2 Domain Knowledge
Specificity
� 72 � 3
Physical Asset Specificity
� 7
� 82 � 4
Site Specificity� 8
� 19
� 9
�
� 9,6
�5, �6, �7, �
19,6
Research Model and a
a: Five control variables: Relational Flexibility (ξ5), Size (ξ6), Length of Association (ξ7), Supplier Dependence (ξ8) and Uncertainty (ξ9) were modeled as distinct independent constructs but are not shown individually in the diagram. Covariances between independent constructs (ϕij) are also omitted in the interests of presentation clarity
Figure 1
Hypothesized Relationships
36
37
TABLE 1:
Descriptive Statistics and Zero Order Correlations (n=211) Construct Mean SD 1 2 3 4 5 6 7 8 9 10
1. Quasi Integration (QI) a 17.15 16.44 1.00
2. Joint Decision Making (JDM)
3.12 1.47 0.23** 1.00
3. Physical Asset Specificity 1.98 1.74 0.02 0.20** 1.00
4. Site Specificity 3.03 1.75 0.17* 0.19** 0.22** 1.00
5. Business Process Specificity
5.50 1.40 0.09 0.17* 0.29** 0.19** 1.00
6. Domain Knowledge Specificity
3.03 1.48 0.29** 0.54** 0.38** 0.27** 0.21** 1.00
7 Relational Flexibility 4.63 1.47 0.20** 0.21** -0.14* 0.14* -0.07 0.21* 1.00
8. Size 3.33 1.30 -0.32** 0.19* 0.06 0.05 -0.04 0.02 -.02 1.00
9. Yrs of Association 17.93 12.57 -0.20** 0.07 0.03 -0.10 -0.06 0.14* 0.06 0.18* 1.00
10. Retailer Replaceabilityb 4.15 1.32 -0.31** -0.07 0.07 -0.19* -0.11 -0.15 -0.27** 0.20* 0.02 1.00
11. Uncertainty 3.68 1.47 -0.16* 0.13* 0.03 0.06 0.03 0.04 0.01 0.21** 0.17* -0.16*
a The mean,sd reported are for the percentage of annual sales to retailer
b retailer replaceablility - is the inverse of the level of dependence Appendix -1
38
TABLE 2 Structural Parameters and Hypotheses
Path / Hypothesis Parameter Standardized Estimate t-value
Business process specificity� quasi integration (H1) γ11 0.01 0.08 Business process specificity� joint decision making (H1) γ21 0.14 1.37* Domain knowledge specificity� quasi integration (H2) γ12 0.33 3.40*** Domain knowledge specificity� joint decision making (H2) γ22 0.23 2.40*** Physical-asset specificity � quasi integration (H3) γ13 0.08 0.58 Physical-asset specificity� joint decision making (H3) γ23 0.28 1.96** Site specificity � quasi integration (H4) γ14 0.09 0.64 Site specificity � joint decision making (H4) γ24 -0.19 -1.34 Relational flexibility quasi integration γ15 0.12 1.10 Relational flexibility joint decision making γ25 0.35 3.01*** Size quasi integration γ16 -0.33 -2.28** Size joint decision making γ26 0.20 1.42* Years of association quasi integration γ17 -0.13 -1.59* Years of association joint decision making γ27 -0.04 -0.52 Retailer Replaceabilitya quasi integration γ18 -0.29 -2.40** Retailer Replaceabilitya joint decision making γ28 -0.09 -0.79 Uncertainty quasi integration γ19 -0.03 -0.31 Uncertainty joint decision making γ29 0.04 0.36
Model χ2(df: 183) =224.34, p< 0.20; GFI,AGFI = 0.89,0.84, CFI= 0.97, NNFI=0.96, RMSEA= 0.027. 90 percent CI for RMSEA = (0.0;0.046)
***:p<.01, **: p<0.05, *: p<0.1 in one sided t tests a: Dependence was measured in terms of its inverse - retailer replaceability
39
TABLE 3: Fit Statistics of Research Model and Alternative Theoretical Models
Research Model
Alternative Model 1: Human Capital Asset
Specificity
Alternative Model 2: Propensity to Invest
Alternative Model 3: Unitary View of
Hybrid Governance Model χ2 224.34(p<0.20) 637.14 (p<.00) 1085.71 (p<.00) 833.74, p<0.00
Model df 183 201 211 192
χ2 diff test χ2 (df: 18)=412.80 p<.00
χ2 (df: 28)=861.37, p<.00
χ2 (df: 9)=609.40 p<.00
GFI/AGFI 0.89/0.84 0.67, 0.55 0.67, 0.57 0.72, 0.59
CFI/NNFI 0.97/0.96 0.67, 0.58 0.57, 0.48 0.79, 0.70
RMSEA 0.027 0.16 0.17 0.15
Result Accept Reject Reject Reject
40
APPENDIX 1
Measures
Physical Asset Specificity:
The extent to which the physical assets used (e.g. manufacturing equipment and machinery) in supplying RetCo are relatively similar or are significantly different from what you use with other retailers:
Scale: Relatively Similar as with other Retailers--Moderately Customized--Significantly Customized for RetCo (7 point Scale)
Adapted from Zaheer and Venkatraman (1994)
Site Specificity
The extent to which the location of the distribution facilities used in supplying RetCo receiving points are relatively similar or are significantly different from what you use with other retailers:
Scale: Relatively Similar as with other Retailers--Moderately Customized--Significantly Customized for RetCo (7 point Scale)
Adapted from Zaheer and Venkatraman (1994)
Business Process Specificity
The extent to which the software and applications used (e.g. billing, inventory management, EDI etc.) in supplying RetCo are relatively similar or are significantly different from what you use with other retailers:
The extent to which the administrative procedures used (e.g. vendor selection, cost accounting procedures etc.) in supplying RetCo are relatively similar or are significantly different from what you use with other retailers:
The extent to which the operating procedures used (e.g. manufacturing, bar-coding, packaging, shipping procedures etc.) in supplying RetCo are relatively similar or are significantly different from what you use with other retailers:
Scale: Relatively Similar as with other Retailers--Moderately Customized--Significantly Customized for RetCo (7 point Scale)
Adapted from Zaheer and Venkatraman (1994)
Domain Knowledge Specificity The extent to which the knowledge and understanding used in planning for new products, programs for RetCo is significantly specific to the relationship (i.e. customized for RetCo) or is relatively similar to what you use with other retailers: The extent to which the knowledge and understanding used in product conception and design for RetCo is significantly specific to the relationship (i.e. customized for RetCo) or is relatively similar to what you use with other retailers: The extent to which the knowledge and understanding used in determining product pricing for RetCo is significantly specific to the relationship (i.e. customized for RetCo) or is relatively similar to what you use with other retailers:
Scale: Relatively Similar as with other Retailers--Moderately Customized--Significantly Customized for RetCo (7 point Scale)
Relational Flexibility
Please indicate your level of agreement or disagreement with the following statements describing the management of your relationship with RetCo: Flexibility in response to changes is characteristic of our relationship Our relationship is flexible in accommodating one another if special problems/needs arise Our firm and RetCo expect to make adjustments in the ongoing relationship to cope with changing circumstances Scale: Strongly Disagree--Neither Agree nor Disagree--Strongly Agree (7 point Scale)
41
Adapted from Noordweir, John and Nevin 1990
Quasi Integration
What % of your sales in the last year did RetCo account for? ____% approx
Adapted from. Zaheer and Venkatraman 1994
Joint Decision Making
Please indicate the extent to which decisions in these issues are made jointly by your firm and RetCo.: Competitive analysis, Strategy formulation Plans for sales promotion, advertising Analyzing market trends, response to promotions etc. Scale: Minimal Joint Decision Making---Moderate---Extensive Joint Decision Making (7 point Scale) Adapted from Heide and John 1990, Zaheer and Venkatraman 1994 Size
What is the annual sales revenue of your firm? < Can$ 5Million Can$ 6-10M Can$ 11-20M Can$ 21-50M Can$51-100M Can$ 101-$500M > Can$
500M
Years of Association
For how many years has your firm been associated with RetCo in Canada? ______ years
Retailer Replaceability We could easily find other customers who would offer as much supplier assistance as provided by RetCo: We could easily find other customers to replace the margin levels with RetCo: We could easily substitute for the loss of reputational effects of being a RetCo supplier: Scale: Strongly Disagree--Neither Agree nor Disagree--Strongly Agree (7 point scale)
Adapted from Noordweir, John and Nevin (1990)
Uncertainty -
What is the likelihood of major changes occurring in this product category over the next 12 months? Extensive Style Changes Major Product Innovations Key Manufacturing/Quality innovations Scale: Very Unlikely --Likely -- Very Likely (7 point scale)
Adapted from Bensaou and Venkatraman (1995)
Perceived Benefits a (Items from survey administered to managers in RetCo buying groups)
Relative to other suppliers of this product category, please indicate the extent to which you are receiving the following benefits as a result of your relationship with the supplier: Learning about customers and markets for your products Creation of new products, product enhancements Scale: Considerably less than other Suppliers, Same as other suppliers, Considerably more than other Suppliers (7 point Scale)
_a retailer managers filled out surveys with respect to 168 specific supplier relationships
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Appendix -2
Parameter Estimates for Research Model
Parameter Standardized
Solution t-value Parameter
Standardized
Solution t-value
Measurement Parameters
λx1,1 0.87 (fixed) λ x 13,7 0.80 (fixed)
λ x2,1 0.74 8.53 λ x 14,8 0.71 (fixed)
λ x 3,1 0.72 8.37 λ x 15,8 0.90 7.64***
λ x 4,2 0.98 fixed λ x 16,8 0.64 6.22***
λ x 5,2 0.64 8.14*** λ x 17,9 0.67 (fixed)
λ x 6,2 0.96 9.55*** λ x 18,9 0.89 6.78***
λ x 7,3 0.80 (fixed) λ x 19,9 0.82 6.86***
λ x 8,4 0.80 (fixed) λ y 11 0.86 (fixed)
λ x 9,5 0.89 (fixed) λ y 22 0.67 8.12***
λ x 10,5 0.79 9.28*** λ y 32 0.95 (fixed)
λ x 11,5 0.61 7.39*** λ y 42 0.64 4.81***
λ x 12,6 0.80 (fixed)
Inter-Construct Correlations
ψ21 0.09 φ45 0.27 2.44***
φ12 0.08 0.89 φ46 0.25 1.92**
φ13 0.24 2.10** φ47 -0.01 -0.05
φ14 0.25 2.23** φ48 -0.21 -1.86**
φ15 -0.01 -0.03* φ49 0.12 1.10
φ16 -0.09 0.80 φ56 0.03 0.28
φ17 -0.01 -0.07 φ57 0.01 -0.03
φ18 0.02 0.24 φ58 -0.37 -3.40***
φ19 0.01 1.06 φ59 -0.04 -0.46
φ23 0.29 2.74*** φ67 0.02 0.23
φ24 0.13 1.30* φ68 0.29 2.47***
φ25 0.11 1.21 φ69 0.45 3.49***
φ26 0.14 1.31* φ78 0.01 0.02
φ27 0.17 2.03** φ79 0.14 0.01
φ28 -0.14 -1.52* φ58 -0.37 -3.40***
φ29 0.06 0.64 φ59 -0.04 -0.46
φ34 0.30 2.32** φ67 0.02 0.23
φ35 -0.24 -2.12** φ68 0.29 2.47***
φ36 0.27 2.11** φ69 0.45 3.49***
φ37 0.01 0.08 φ78 0.01 0.02
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Parameter Standardized
Solution t-value Parameter
Standardized
Solution t-value
φ38 0.22 1.95** φ79 0.14 0.01
φ39 0.18 1.57*
***: p<.01, **: p<.05, *:p<.1 in one sided t tests
44
Author Bios Mani Subramani is an Assistant Professor in the Information and Decision Sciences (IDSc) department at the Carlson School of Management, University of Minnesota. His research focuses on the strategic role of information technologies in organizations. His current areas of research are the management of interorganizational relationships, knowledge management and electronic commerce.
N. Venkatraman is the David J. McGrath Jr. Professor of Management at Boston University School of Management. His research interests are at the intersection of strategic management and information technology.
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