Safeguarding Investments in Asymmetric Interorganizational ... · Safeguarding Investments in Asymmetric Interorganizational Relationships: ... Asymmetric Interorganizational Relationships:

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.

mailto:[email protected]

mailto:[email protected]

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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).


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.


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:



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:



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:


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

42

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

43

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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