the effect of operations strategy on supplier-customer ... · and suppliers’ financial...

The Effect of Operations Strategy on Supplier-Customer

Relationships and Supplier’s Financial Performance

By

Yoon Hee Kim

Urban Wemmerlöv

University of Wisconsin-Madison

ISBM Report 4-2009

Institute for the Study of Business Markets

The Pennsylvania State University 484 Business Building

University Park, PA 16802-3603 (814) 863-2782 or (814) 863-0413 Fax

www.isbm.org, [email protected]

Working Paper Yoon Hee Kim and Urban Wemmerlöv

June 4, 2009 1 of 52

The Effect of Operations Strategy on Supplier-Customer Relationships

and Suppliers’ Financial Performance

Yoon Hee Kim and Urban Wemmerlöv

Erdman Center for Operations and Technology Management Wisconsin School of Business

University of Wisconsin-Madison 975 University Avenue

Madison, WI 53706

WORKING PAPER

ACKNOWLEDGEMENT

Funding for this Research was provided by the Institute for the Study of Business Markets of

The Pennsylvania State University. However, the contents of this research report reflect the

views of the researchers who are solely responsible for the facts and the accuracy of the data

presented herein.


June 4, 2009 2 of 52

The Effect of Operations Strategy on Supplier-Customer Relationships

and Suppliers’ Financial Performance

ABSTRACT

As a key to a firm’s success, effective supply chain management practices have received

enormous attention from both academics and practitioners. Especially, a shift in a paradigm of

supplier-customer relationships from an “arm’s length” type of interaction to a “closer tie”

relation in the manufacturing industry has been widely reported (Frazier et al. 1988; Heide and

John 1990; The Economist 2006). The literature suggests that a move towards to a close

customer-supplier relationship is mutually beneficial for the parties involved. It is argued, for

example, that close relationships enhance the financial performance of the buyer firms through

reduced costs and increased profits (see Noordewier et al. 1990; Gosman et al. 2004). Yet, the

benefits of close supplier-manufacturer relationships accruing to supplier firms have not been

well documented.

In this study, we empirically assess the impact of close relationships between suppliers and

customers on the financial performance of the supplier. Specifically, we are interested in whether

and how a supplier’s strategic choices in the operations area affect its relationship with a

customer and help balance risks and rewards in the relationship. Cross-sectional data are

collected by survey (available online and by mail) from suppliers in the manufacturing industry.

The direct and mediated relationships among the capabilities associated with operations

strategies, the supplier-manufacturer relationships, and the financial performance of suppliers are

tested using structural equation modeling.

Our findings from this study are expected to have several important managerial implications.

Firstly, our integrative study shows a link between the operations capabilities and the supplier-

customer relationships. Secondly, our study suggests a way to simultaneously enhance the

suppliers’ relationships with their customers and the profitability associated with these

relationships. Lastly, our study verifies a link between close supplier-customer relationships and

the suppliers’ financial performance from the relationships.


June 4, 2009 3 of 52

1. INTRODUCTION

For more than a decade, there has been a large and growing interest, among academics and

practitioners alike, in the value of effective supply chain management (SCM) practices. The

literature suggests that a move towards to a close relationship between suppliers and customers is

mutually beneficial for both parties. This notion has been widely accepted among original

equipment manufacturers (OEMs) in the U.S. As a result, the leading OEMs have reduced their

supplier base in recent years and reportedly developed closer relationships with a selected few in

the form of strategic alliances or partnerships (McCutcheon and Stuart 2000; Johnston et al. 2004;

Narayandas and Rangan 2004; The Economist 2006).

It has been argued that close relationships enhance the financial performance of the buyer firms

through reduced costs and increased profits (Noordewier et al. 1990; Cannon and Homburg

2001). Yet, the benefits of close supplier-buyer relationships accruing to supplier firms are

seldom explicitly stated (New 2004), p.81) nor empirically demonstrated in the large body of

research on SCM (one exception is Kalwani and Narayandas 1995). This glaring gap in the

literature is the starting point of this research.

It is often assumed, based on the notion of rational managers, that supplier firms will establish

and maintain exchanges with customers only if profits can be extracted. Otherwise, the

relationships will be terminated (Kalwani and Narayandas 1995);(Helm et al. 2006). Contrary to

this assumption, however, is anecdotal evidence suggesting that unprofitable customer

relationships may not be uncommon among suppliers (Helm et al. 2006), possibly due to power

unbalances in the relationships (Bunkley 2006). Such contradicting anecdotes to the seemingly

intuitive assumption of profit-seeking warrant a further investigation into the benefits of close

relationships accruing to supplier firms.

In brief, this study focuses on strategic and financial issues associated with supplier-customer

relationships as viewed from the supplier’s perspective. We seek to understand what a supplier

firm can do to maintain and enhance its profitability relative to individual customers. Specifically,

we investigate whether and how a supplier’s strategic choices in the operations area affect its


June 4, 2009 4 of 52

relationship with a customer and help balance risks and rewards in the exchange. The general

research questions to be answered in this study are the following: (1) Are close relationships with

customers beneficial for supplier firms?, (2) What is the impact of power/dependence imbalances

on supplier-customer relationships and on the suppliers’ profitability from the customer

relationships?, and (3) How do the suppliers’ choices of operations strategy affect their

relationships with customers?

We primarily focus on the supplier-manufacturer dyad1 for a couple of reasons. Firstly, anecdotal

evidence suggests that a new relationship paradigm (e.g., partnerships, alliances, etc.) has been

widely accepted among original equipment manufacturers (OEMs; The Economist 2006) but the

benefits accruing to supplier of manufacturing firms, to our knowledge, have not been much

documented. Rather, there is anecdotal evidence suggesting that suppliers may be exploited in

power-unbalanced relationships (Wilke 2004; Bunkley 2006). Secondly, anecdotal evidence also

suggests that many part and material suppliers are forced by customers to adopt new process

improvement techniques (e.g., lean practices; see Velocci 1999) but that they are not purportedly

rewarded for their efforts (Balakrishnan et al. 1996; Vantuono 2000). Thus, the phenomena of

our research interest seem to be present in supplier-manufacturer relationships.

The rest of this paper is organized as follows. In section 2, we present a review of relevant

literature. In section 3, the research method is explained. We present the data analysis in section

4, the results in section 5, and discussions of the findings in section 6. In section 7, we

summarize the contributions of the study and discuss the limitations and future research in

section 8.

1 We use the term “supplier-manufacturer relationships” in a narrow sense to distinguish them from general supplier-buyer relationships in a supply chain which can also include relationships between manufacturers and distributors and companies in the service sector (Kalwani and Narayandas 1995); (Goffin et al. 2006), p.190). Hereafter, we call the exchange partners in a relationship “supplier” and “manufacturer.” The term “customer” is used interchangeably with “manufacturer.”


June 4, 2009 5 of 52

2. Literature Review

2.1 Customer-Supplier Relationships

Firm Relationships and Related Theories

An accepted paradigm in the supply chain management (SCM) literature views the ideal

relationships between supplier and customer firms as cooperative for mutual benefits (see

Harland, Knight and Cousins (2004) for a review of supply chain relationship perspectives held

by different business disciplines). Cooperation often arises in the context of firms’ specific needs

(e.g., cost reduction or value addition) and “unfolds through ongoing interactions” between firms

(Heide and Miner 1992, p. 266). Domains of interactions where cooperation takes place include

information sharing and joint decisions. Information sharing can be defined as the degree to

which each party discloses information that may facilitate the other party’s activities (Frazier et

al. 1988; Heide and Miner 1992; Sousa 2003; Johnston et al. 2004). Joint decisions can be

defined as the degree to which each party penetrates the other party’s organizational boundaries,

and include joint efforts to improve, for example, production costs, demand forecasts and

product designs (Frazier et al. 1988; Heide and John 1990; Heide and Miner 1992; Jap 1999;

Sousa 2003; Johnston et al. 2004).

The development of the cooperative paradigm has been influenced by theories in various

disciplines (see Giannakis, Croom and Slack (2004) for a review of theoretical foundations for

supply chain paradigms). For this study, we will borrow from resource dependence theory,

transaction cost analysis, and governance value analysis to develop a conceptual framework that

integrates resources, the supplier-customer relationships, and performance as seen from the

suppliers’ perspective.

Resource dependence theory takes the view that a business relationship is a social exchange of

critical resources with mutual dependency among the exchange partners. Thus, the survival and

growth of organizations largely depend on the ability to secure critical resources from the

external environment (Emerson 1962; Pfeffer and Salancik 1978; Casciaro and Piskorski 2005).

But a relationship between organizations is not free. Transaction cost analysis (TCA) suggests

that every transaction has a cost. These costs are incurred for adaptation, performance evaluation


June 4, 2009 6 of 52

and safeguarding, and are associated with uncertainty, opportunism, and transaction specific

assets (TSAs) invested in the relationship (Williamson 1975, 1985, 1996; Rindfleisch and Heide

1997). Transaction specific assets refer to the assets specialized to service the particular needs of

the exchange parties (Williamson 1996). Firms invest in TSAs in order to create additional value

from an exchange above what standard product and service offerings can do (Ghosh and John

1999). Examples of TSAs include the development of idiosyncratic knowledge, the provision of

dedicated human resources and training, and capital investment in specialized equipment and

facility improvement (Williamson 1996).

Although resource dependence theory and transaction cost analysis depart from different points

of view (sociology and new institutional economics, respectively), they have something in

common. While resource dependence theory focuses on ex ante mutual dependence between

exchange partners due to critical resources, transaction cost analysis assumes that two parties are

initially independent but develop bilateral dependence ex post due to relationship-specific assets

invested over the course of the relationship (Heide 1994, p. 73; Casciaro and Piskorski 2005, p.

174). Despite these different views2, however, both theories recognize the existence of

interdependency between exchange partners and the importance of securing valued resources

from environmental and behavioral uncertainty (Heide 1994). Specifically, based on utilitarian

assumptions of self-interested behaviors of exchange partners, transaction cost analysis argues

that TSAs raise the cost of safeguarding against a behavioral uncertainty of an exchange partner

such as an opportunistic behavior where one party may exploit the other for unilateral benefits

(Heide and John 1990; Heide 1994; Rindfleisch and Heide 1997; Bensaou and Anderson 1999;

Ghosh and John 1999, 2005). Being unique to a relationship, and possessing little or no value

upon the relationship termination, TSAs are often viewed as “valuable but vulnerable”

investments (Ghosh and John 1999; Wathne and Heide 2004; Ghosh and John 2005).

Combining the resource and transaction cost perspectives into a strategic point of view, Ghosh

and John (1999) proposed a governance value analysis (GVA) framework that links resources,

2 Williamson (1996, p.239) points out “the myopic vs. farsighted view of contact” as the fundamental difference between resource dependency and transaction cost analysis. He argues that in resource dependence theory, parties to contracts are assumed to be myopic and therefore the hazards of dependency cannot be safeguarded. On the other hand, transaction cost analysis believes that parties are farsighted enough to foresee and safeguard the hazards of dependency ex ante.


June 4, 2009 7 of 52

positioning strategy, TSAs and governance. They argue that a firm creates potential market value

through a unique positioning and can claim those values through a competitive advantage based

on firm-specific resources. In an effort to achieve competitive advantage in the market, firms

align themselves with exchange partners (i.e., customers and suppliers) and create joint values,

such as cost reduction and/or value addition, through investments in TSAs. While creating

maximum values from the market, they argue that firms should safeguard their share of values

jointly created as well as their investments in TSAs against opportunism through strategic

selection of relationship governance. For example, the authors found, in a later study on

industrial alliances, that OEMs – given a high level of specific investments – achieve a high level

of cost reduction from less flexible contracts with their suppliers while achieving a high level of

end-product enhancement from more flexible contracts (Ghosh and John 2005). Based on these

findings, they suggest that OEMs take different “governance value engineering” approaches to

supplier relationship management depending on their primary pursuit of strategic outcomes (i.e.,

cost reduction vs. product enhancement).

GVA and the Perspectives of Suppliers

Although GVA offers an appealing framework for the analysis of values created and claimed by

parties in a relationship, and has a demonstrated applicability to customer firms, it has limitations

when applied to suppliers. Given that business relationships are most often initiated by customer

firms, and that these tend to be larger than their suppliers, there is a natural and noted concern

regarding power/dependence imbalances between the parties and the possibility of customers’

exploitation of suppliers (Lyons et al. 1990; Balakrishnan et al. 1996; Koufteros and Kunnathur

1996; Hingley 2005). This concern appears to be growing in recent years as high buyer

concentrations have resulted from mergers and acquisitions in the 1990’s. Large buyer firms (or

small groups of large buyers), and their power in the market, raise the specter of monopsony (or

oligopsony) which is the mirror image of monopoly (or oligopoly) (Wilke 2004). It implicitly

suggests that in many cases supplier protection through vertical integration or better contracts

may not be available since suppliers are often not large enough to consider vertical integration,

nor do they have sufficient bargaining power to extract safeguards through contractual

agreements. Thus, the idea of “engineering” the relationship governance as a safeguard against

opportunistic behaviors of customers may be of little relevance to many suppliers.


June 4, 2009 8 of 52

There has been, as Ganesan (1994) pointed out, an implicit assumption that supply chain

members behave in a similar fashion regardless of their role in a relationship. It can be surmised,

however, that customers and suppliers have different perceptions and behavioral intentions

depending on their roles (Ganesan 1994; Gundlach and Cadotte 1994; Steinman et al. 2000;

Wathne et al. 2001). Ganesan (1994), for example, observed that retailers and vendors have

slightly different attitudes toward dependence and long-term relationship. Retailers are seldom

engaged in a long-term relationship with a dependent vendor while vendors tend to maintain a

long-term relationship with a highly dependent retailer. Wathne, Biong and Heide (2001) also

observed that customers and suppliers in the commercial banking industry show slight

differences in their views on customers’ switching intentions. Customers view the product

attributes such as price and product breadth as most important for their switching intentions

while suppliers see price and “interpersonal relationships” as most important to the customers.

Thus, firms appear to have different perceptions and attitudes toward an exchange relationship

depending on their roles. In particular, supplier firms appear to be somewhat more influenced by

social factors such as dependence and interpersonal relationships than are customer firms.

In this vein, the GVA framework seems to reflect, from the suppliers’ perspective, a mechanical

or non-social view of supplier-customer relationships because it views governance value as a

simple function of economic factors. Expressed in Jap’s pie analogy (1999), the factors include

the size of a pie (the overall values jointly created), the size of an individual slice (the shared

value for a party), and the strategically chosen slicing rule (governance) depending on a kind of

pie (e.g., desired outcomes).Wilson (1995, p.342), however, argues that “the sharing of value,” a

supposedly rational economic behavior, “is likely a function of the power/dependence

relationship modified by the degree of structural bonding present in the relationship.” Structural

bonding is described as “forces to create impediments to the termination of the relationship” and

is affected by the levels of TSAs and adaptation between parties. Thus, Wilson (1995) views that

the power/dependence relation has a first order (direct) effect on the sharing of value between

parties while TSAs and adaptation have a second order (indirect) effects through modification.

The “modifying” effect of TSAs and adaptation can take place through either mediation or

moderation. Regardless, this view is in line with transaction cost analysis’ notion of TSAs as a

source of interdependency between exchange partners (Heide 1994). Hence, our conjecture is


June 4, 2009 9 of 52

that suppliers’ ability to reap satisfactory rewards from relationships can depend on the

power/dependence relations with their customers, especially given suppliers’ inclination to

consider social factors.

2.2 Operations Strategy and Its Organizational Impact

Capability-Based Strategies

Operations strategy can be defined as a “pattern of decisions” in allocating resources across

operational decision areas (Mintzberg 1978; Wheelwright 1984; Hayes et al. 2005). Resources

are tangible and intangible inputs into the value creation process and include, but are not limited

to, equipment, skills of employees, and technology (Grant 1991; Ghosh and John 1999; Hayes et

al. 2005). Yet, the extended resource-based view argues that resources are necessary but not

sufficient to create heterogeneous sustainable competitive advantages across firms and times

because resources can be readily obtained through various means (e.g., acquisition, development,

etc.) by other firms (Grant 1991; Hayes et al. 2005). Rather, capabilities, defined as “the capacity

for a team of resources to perform” productive tasks or activities (Grant 1991, p. 119), are

viewed as the main source of competitive advantage.

Since capabilities are interwoven in the behaviors of people and operating processes within a

firm, they are often complex, dynamic and firm-specific in nature (Hayes et al. 2005). Creating

capabilities requires coordination between people and other resources through organized,

repetitive actions such as practices and routines governing the flows of materials and information

throughout the value creation process (Grant 1991). Thus, it has been argued that a firm’s

capabilities should be measured by a “bundle of routines” or practices (Peng et al. 2008).

Therefore, operations strategy is viewed in this study as a pattern of decisions on the selection

and development of capabilities through a variety of strategic choices regarding operational

practices and processes designed to manage the flows of materials and information.

As a functional strategy, the role of operations strategy is to support a company’s competitive

priorities as dimensions of advantage relative to its competitors. There is broad agreement, albeit

with semantic differences, on the following operations-oriented competitive priorities in the

literature: cost, quality, delivery, flexibility and innovation (Miller and Roth 1994; Ward et al.


June 4, 2009 10 of 52

1995; Vickery et al. 1997; Ward et al. 1998). These can be supported by different capabilities,

with each capability developed through the adoption of single or multiple practices and processes.

Some capabilities (e.g., conformance quality, cost efficiency and delivery dependability) are

considered complementary and need to be developed cumulatively (Ferdows and De Meyer 1990;

Noble 1995) or simultaneously (Miller and Roth 1994) to achieve competitive advantage. Based

on the literature, we identify six dimensions of capability that can support the aforementioned

competitive priorities. To avoid confusing capabilities with competitive priorities, we label the

capability dimensions as follow: conformance quality, cost efficiency, delivery dependability,

flexible responsiveness, new product development and new product introduction (Noble 1995;

Ward et al. 1995; Vickery et al. 1997; Ittner and Larcker 1997b; Ward et al. 1998; Ward and

Duray 2000).

The literature suggests that operations capabilities can be viewed from two broad strategic

focuses: cost reduction and innovation (Miller and Roth 1994; Ward and Duray 2000; Frohlich

and Dixon 2001). Although seemingly simplistic, this dichotomous distinction represents a

firm’s behavioral tendency to allocate resources for “exploitation and exploration” (March 1991;

Benner and Tushman 2003) or “improvement and innovation” (Peng et al. 2008). Cost reduction

strategy can be defined as a focus on cost savings through internal operations efficiency and

manifests itself through an emphasis on three capabilities: conformance quality, delivery

dependability and cost efficiency (Ferdows and De Meyer 1990; Noble 1995). Cost efficiency

refers to the ability to lower production costs through efficient operations and process

management. Conformance quality refers to the ability to make products with consistent quality

that meet agreed-upon performance standards. Delivery dependability refers to the ability to

deliver on time in the right amount and product mix as specified by customers. Management

practices adopted to strengthen these capabilities include lean production, six sigma, and quick

response manufacturing (and associated tools that are used by these improvement methodologies)

(Hall 1987; Womack et al. 1990; Deming 1992; Suri 1998).

On the other hand, innovation strategy can be defined as an emphasis on assisting customers

through flexible operations and concurrent product/process development, and manifests itself

through the nurturing of three other capabilities: flexible responsiveness, new product


June 4, 2009 11 of 52

development and new product introduction. Flexible responsiveness3 refers to the ability to

modify products and associated processes for non-routine demands. New product development

refers to the ability to develop new products - with enhanced styling, features and performance -

for ease of manufacturing. New product introduction refers to the ability to rapidly introduce

new products into full scale production. Innovation can be facilitated by the combination of

internal practices for flexibility such as training of engineers and workers and effective

integration of various functional expertise within and across firms (Hall 1987, pp. 9-13). New

products, for example, can be effectively developed through cross-functional teams and

concurrent product/process development, and be efficiently introduced at desired quality and

time to the market using flexible operating capabilities (Hayes et al. 2005, pp. 195–208).

The Impact of Capability-Based Strategies

Capabilities contribute to business performance either individually or collectively (Noble 1995;

White 1996). Thus, both of the two main strategic focuses (i.e., cost reduction and innovation)

can help firms compete on operational excellence in the marketplace (Miller and Roth 1994). Yet,

each strategy may create a different path to superior business performance through different

earning mechanisms, different levels of closeness in customer relationships, and different levels

of investments in transaction specific assets. Although the ultimate purpose of both strategies is

to increase profitability, suppliers pursuing a cost reduction strategy may achieve this goal

through increased sales volume at lowered margins without developing a close relationship with

3 Flexibility is a multi-dimensional construct with several elements. Upton (1994), for example, defined flexibility as “the ability to change or react with little penalty in time, effort, cost or performance” (p. 73) and identified three elements: range, mobility and uniformity. D'Souza and Williams (2000) identified four dimensions of flexibility (i.e., volume, variety, process, and materials handling flexibilities) and viewed each dimension as having two elements (i.e., range and mobility). Similarly, Koste and Malhotra (1999) identified six components of flexibility (i.e., machine, labor, material handling, mix, new product, and modification) with each component having four elements (i.e., range-number, range-heterogeneity, mobility and uniformity). In a later study, they showed that the four elements of the six components can be grouped into two factors representing different dimensions of flexible responses: scope (as the sum of two range elements across six components) and achievability (as the sum of mobility and uniformity across six components) (Koste et al. 2004). Lastly, Anand and Ward (2004) studied the strategic fit between environment and two flexibility dimensions (i.e., range and mobility). They argued that in unpredictable environments the mobility-flexibility is desired to handle non-routine and innovative demands efficiently whereas in volatile but more predictable environment the range-flexibility is desired to respond quickly within a broad range of product features and volumes. Despite interrelations between range and mobility (Anand and Ward 2004), it is argued that most firms have flexibility in one dimension or the other (i.e., scope or achievability), but not in both simultaneously, due to resource constraints (Koste et al. 2004). By “flexible responsiveness” we are referring to Koste et al.’s (2004) achievability or Anand and Ward’s (2004) mobility as a firm’s adaptive maneuverability in unpredictable environments.


June 4, 2009 12 of 52

the customer. The cost reduction strategy is generally associated with products in a mature stage

of the product life-cycle and with high degrees of standardization (Miller and Roth 1994). Such

products are usually produced by production lines or continuous flow shops for high

conformance quality and cost efficiency (Safizadeh et al. 1996). Statistical quality control and

various process improvement tools are frequently employed (Ferdows and De Meyer 1990), but

these practices do not require customer involvement (Ittner and Larcker 1997a) or transaction

specific assets (Bensaou and Anderson 1999). Suppliers pursuing a cost reduction strategy pay

relatively little attention to after-sales service and R&D (Miller and Roth 1994). Rather, they

focus on superior inventory and quality control practices to achieve cost savings and may pass

these on to customers in anticipation of sales volume increases at the expense of unit margins

(Kalwani and Narayandas 1995; Cannon and Homburg 2001).

In contrast, suppliers pursuing an innovation strategy may increase profitability through

increased sales volumes/market shares and premium pricing at high margins, while also pursuing

close relationships with customers. The innovation strategy is associated with frequently

changing or highly customized products (Miller and Roth 1994) which are usually produced by

shops designed for product flexibility (Safizadeh et al. 1996). Suppliers pursuing an innovation

strategy tend to focus on innovative design features in their products, in addition to conformance

quality, and introduce new products in both old and new markets to achieve market share

increases (Miller and Roth 1994). High design quality facilitates the development and production

of new products by reducing engineering changes and contributes to the timely introduction of

new products at desired performance quality and cost (Fynes and Voss 2001). Although time-to-

market, design quality, and price are all important aspects of the success of a new product, time-

to-market and design quality appear to be most critical for customer satisfaction (Tatikonda and

Montoya-Weiss 2001). The result may be high sales growth and improved financial returns

(Ittner and Larcker 1997b) through increased market share and premium pricing (Phillips et al.

1983).

Product customization and new product development, however, require some cooperation

between suppliers and customers and may require a high level of investments in TSAs. Highly

customized products are usually produced in small batches by flexible plants (Safizadeh et al.


June 4, 2009 13 of 52

1996), and often require custom designed tools (e.g., jigs and dies) and additional training of

employees in engineering, production and service (Dyer 1996; Bensaou and Anderson 1999).

Thus, suppliers pursuing an innovation strategy tend to invest heavily in R&D and concurrent

product/process development, and show strong concerns for after-sales-service (Miller and Roth

1994). In addition, the successful introduction of a new product is facilitated by the use of cross-

functional teams and the application of design tools, including quality function deployment,

design of experiments, and failure mode and effects analysis (Ittner and Larcker 1997b; Hayes et

al. 2005). Such practices involve not only different functions, such as sales, design and

manufacturing, but also customers and suppliers (Ittner and Larcker 1997b). The involvement of

the latter groups leads to cooperative activities such as information sharing and joint decisions on

product design and production planning (Bensaou and Anderson 1999; Sousa 2003).

In sum, suppliers pursuing an innovation strategy are likely to develop stronger “structural

bonding” with their customers than those pursuing a cost reduction strategy. Structural bonding,

in turn, modifies the power/dependence relations between suppliers and customers (Wilson

1995). Since power/dependence is assumed to have a first-order effect on value sharing, while

structural bonding has a second-order effect (Wilson 1995), we chose here to focus on the effect

of a supplier’s operations strategy on its power/dependence relation rather than on its effect on

structural bonding. Accordingly, we next discuss the power/dependence relation between

suppliers and customers and its impact on value creation and value sharing.

2.3 The Effects of Power Imbalance and Mutual Dependence

Power/Dependence Relations

According to Emerson (1962), a customer depends on a supplier if the customer’s goal

achievement is facilitated by the supplier’s actions. In turn, the supplier has the power to control

or influence the customer because power is the flip side of dependency. It has been suggested

that interdependence has two dimensions: mutual dependence, or a cohesion of

power/dependence, and power imbalance, or a power/dependence advantage or disadvantage

(Emerson 1962; Gundlach and Cadotte 1994; Heide 1994; Kumar and Scheer 1995; Casciaro and

Piskorski 2005; Piskorski and Casciaro 2006). There can be different levels of power imbalance

in a dyad for any given level of mutual dependence. Conversely, there may exist various levels


June 4, 2009 14 of 52

of mutual dependence in a dyad for any given level of power imbalance (Casciaro and Piskorski

2005; Piskorski and Casciaro 2006). This is illustrated in Figure 1.

Given three possible levels of supplier and customer dependence in a relationship, the

dependence matrix depicts all possible power-dependence structures in a dyad. Power imbalance

(PI) represents a supplier’s dependence on a customer relative to a customer’s dependence on the

supplier – the higher the PI value, the greater the supplier’s power disadvantage relative to the

customer (Piskorski and Casciaro 2006). Mutual dependence (MD), on the other hand, represents

the bilateral dependency in a dyad and is defined as the interaction of the supplier’s and the

customer’s dependencies on each other – the greater the MD value, the stronger the parties’

mutual dependency (Heide 1994; Piskorski and Casciaro 2006). Emerson (1962) has argued that

power/dependence is a property of the social relation rather than an attribute of the actor. When a

supplier’s (S) and a customer’s (C) dependence are considered individually, dependence is an

attribute of a party and does not explicitly represent a social property of the power/dependence

relation (Casciaro and Piskorski 2005). Therefore, we need to consider S’s dependence in

relation to C, and C’s in relation to S, in order to fully understand the power/dependence

relations in a dyad (Emerson 1962; Gundlach and Cadotte 1994; Heide 1994; Casciaro and

Piskorski 2005; Piskorski and Casciaro 2006). In this respect, power imbalance and mutual

Figure 1: Power-Dependence Matrix

Low (1) Medium (2) High (3)

Low (1) Configuration 1: Configuration 2: Configuration 3:

PI: 1 PI: 2 PI: 3

MD: 1 MD: 2 MD: 3

Medium (2) Configuration 4: Configuration 5: Configuration 6:

PI: 1/2 (0.5) PI: 1 PI: 3/2 (1.5)

MD: 2 MD: 4 MD: 6

High (3) Configuration 7: Configuration 8: Configuration 9:

PI: 1/3 (0.3) PI: 2/3 (0.6) PI: 1

MD: 3 MD: 6 MD: 9

Power Imbalance (PI) = Supplier ÷ Customer (Casciaro and Piskorski 2006)Mutual Dependence (MD) = Customer × Supplier (Heide 1994; Casciaro and Piskorski 2006)

Supplier's Dependence on Customer

Cu

sto

mer

's D

epen

den

ce o

n

Su

ppl

ier

Source: The matrix is adopted from Casciaro and Piskorski (2005) and modified for our purposes.


June 4, 2009 15 of 52

dependence are defined by relative terms (i.e., a comparison and an interaction) rather than by

individual terms (a supplier’s or a customer’s dependence on the other party).

It has been argued that the strength of mutual dependence and power imbalance affect each

party’s motivation and behavior in an exchange (Emerson 1962; Buchanan 1992; Gundlach and

Cadotte 1994; Heide 1994; Kumar and Scheer 1995; Casciaro and Piskorski 2005; Piskorski and

Casciaro 2006). Given the low mutual dependence in configurations 1, 2 and 4 in Figure 1, for

example, there may be weak incentives for the parties to cooperate with each other, or to

continue a relationship when conflict arises, because each party has little at stake. On the other

hand, in high mutual dependence relationships, such as configurations 6, 8 and 9, the parties

have strong incentives to cooperate even in the presence of conflict because the relationship

cannot easily be replaced without sacrifice. Power imbalance, however, still can exist in highly

interdependent relationships such as configurations 6 or 8. Under conditions of high mutual

dependence, therefore, opportunism will be a concern for the weaker party, especially when this

party faces high costs of switching partners (Heide 1994; Kumar and Scheer 1995; Casciaro and

Piskorski 2005; Piskorski and Casciaro 2006).

Operations Strategy, Mutual Dependence and Power Imbalance

Earlier we discussed how mutual dependence is viewed by resource dependence theory and

transaction cost analysis. Ex ante mutual dependence in a relationship is based on critical

resources to be exchanged and ex post mutual dependence increases as relationship-specific

assets increase (Heide 1994, p. 73; Casciaro and Piskorski 2005, p. 174). This suggests that even

if there is asymmetry in the dependence between exchange partners at the early stages of a

relationship, their mutual dependence would increase as each party’s investment in transaction

specific assets increases over time. From the supplier’s perspective, mutual dependence increases

and power imbalance decreases as a customer’s dependence on the supplier increases (see Figure

1). A customer’s dependence – given any level of its investment in transaction specific assets –

will increase as the value of critical resources (products and services) from the supplier increases.

Further, the value of a supplier’s product and services to a customer will presumably improve

through the development and strengthening of its capabilities. In this regard, a supplier’s

capabilities – whether associated with a cost reduction or an innovation strategy – will increase


June 4, 2009 16 of 52

its customers’ dependence on the supplier and, in turn, decrease the power imbalance while

increasing the mutual dependence. Thus, our first hypothesis is as follows:

Hypothesis 1: As the degree to which a supplier implements capabilities - associated with

either a cost reduction or an innovation strategy - increases, the power imbalance

decreases while the mutual dependence increases in a relationship with a customer.

The level of a customer’s dependence may vary depending on how critical the supplier’s

resources are to the customer, and on the level of the customer’s investments in transaction

specific assets (Ghosh and John 2005). For example, customers may have intentions to expand

future business volumes with suppliers that offer a cost advantage (Cannon and Homburg 2001).

However, they may not necessarily be motivated to invest in TSAs, nor to develop closer

relationships with those suppliers, because the products and/or services can usually be delivered

through standard procedures (Bensaou and Anderson 1999). The expected outcomes can thus be

readily safeguarded by ex ante contracts and ex post monitoring (McCutcheon and Stuart 2000;

Ghosh and John 2005). Similarly, suppliers pursuing a cost reduction strategy may not be

motivated to invest in TSAs nor to develop close relationships by increasing customer

involvement because they can achieve cost savings through internal operations efficiencies.

Rather, such suppliers often attempt to increase sales by offering low prices to customers

(Kalwani and Narayandas 1995; Cannon and Homburg 2001).

Customers seeking the innovation advantage from suppliers, on the other hand, need to build

close relationships with them in order to reduce the risks of uncertainty in downstream markets

(Ghosh and John 2005). In the presence of downstream uncertainty (e.g., unpredictable changes

in consumer demand, i.e., the demand from the customer’s customers), the ability of customers

to respond to the demand changes is highly contingent on suppliers’ ability and willingness to

cope with change requests from upstream. Thus, customers often use qualification programs to

assure that suppliers’ operating capabilities can accommodate such needs. They can also make

investments in TSAs to increase suppliers’ willingness to cooperate while also urging the

suppliers to do the same to reduce their opportunism (Wathne and Heide 2004). High level of

TSAs is believed to increase mutual dependence (Heide and John 1990) and cooperation


June 4, 2009 17 of 52

between the exchange partners (Heide and Miner 1992). More specifically, customers tend to

increase their TSA investments to support non-standardized tasks such as the development of

highly customized or new products (Bensaou and Anderson 1999). They also increase their

suppliers’ involvement in the development process through cross-functional teams and the use of

various design practices, such as quality function deployment, design of experiments, and failure

mode and effects analysis (Ittner and Larcker 1997b). Similarly, suppliers pursuing an innovation

strategy are likely to make investments in TSAs, such as engineering time and custom design

tools, and develop close relationships with customers through cooperation in the product

development process. Thus, suppliers pursuing an innovation strategy not only have a higher

dependence on customers than those pursuing a cost reduction strategy, but they also increase

the customers’ dependence on them to a greater extent than those pursuing a cost reduction

strategy. Thus, suppliers pursuing an innovation strategy are likely to show a lower power

imbalance and a higher mutual dependence than those pursuing a cost reduction strategy. Based

on this, our second hypothesis is as follow:

Hypothesis 2: Capabilities associated with an innovation strategy have more favorable

impacts on a supplier’s power imbalance and mutual dependence relative to its customers

than capabilities associated with a cost reduction strategy.

Mutual dependence and power imbalance, in turn, may have different effects on the closeness of

a supplier-customer relationship and a supplier’s ability to claim its share of value jointly created

from the relationship. Recall that governance value analysis suggests that firms form alliances

with exchange partners and make investments in transaction specific assets in order to jointly

create values for the end-customers (Ghosh and John 1999, 2005). The levels of mutual

dependence and power imbalance will lead to different dynamics in terms of interactions during

cooperation and, in turn, to different outcomes with respect to joint value creation and sharing.

As power imbalance increases in a relationship, for example, conflict is likely to arise because

the more powerful party has an increasing ability to appropriate the benefits from the relationship

(Piskorski and Casciaro 2006) and less motivation to refrain from exercising power through

coercion (e.g., negative statements and unilateral demands) (Gundlach and Cadotte 1994; Kumar

and Scheer 1995). In the presence of high power imbalance, commitment might be rare due to a


June 4, 2009 18 of 52

high potential for opportunism and a lack of trust between the firms (Kumar and Scheer 1995).

As a result, a high power imbalance decreases the willingness to accommodate the needs of the

exchange partner (Heide 1994), thereby impeding the weak party’s ability to achieve its desired

outcomes (Buchanan 1992). From a supplier’s perspective, specifically, this suggests that as its

power imbalance decreases the supplier can develop closer relationships with customers through

information sharing and joint decisions, and can jointly create higher values for the end-

customers. Furthermore, a supplier’s increased relative power will improve its financial gains

from the relationship because the supplier will have greater bargaining power when it comes to

dividing the value created in the relationship with the customer. Thus, our third hypothesis is as

follows:

Hypothesis 3a: As a supplier’s power imbalance reduces, the supplier-customer

relationship becomes closer through expanded information sharing and more joint

decisions.

Hypothesis 3b: As a supplier’s power imbalance reduces, the supplier’s profitability from

the relationship with the customer increases.

As mutual dependence increases – especially in a symmetric manner as in configurations 5 and 9

in Figure 1 – conflict is likely to be averted or resolved because both parties are motivated to

maintain the relationship (Gundlach and Cadotte 1994; Kumar and Scheer 1995). High mutual

and symmetric dependence fosters an atmosphere in which commitment and trust can increase

(Kumar and Scheer 1995). Inter-organizational commitment can initiate trust (Ganesan 1994)

and, in return, trust can encourage commitment between the parties (Jap 1999; Narayandas and

Rangan 2004). As mutual dependence increases, the parties will engage in more frequent

exchanges with each other (Piskorski and Casciaro 2006) and adjust to the needs of the other

party under a low risk of opportunism, especially when mutual dependence increases in a

symmetric manner (Heide 1994). Thus, mutual dependence improves cooperation between

parties and can result in greater joint value (Jap 1999). From the supplier’s perspective, this

suggests that as mutual dependence increases in a symmetric manner, suppliers can develop

closer relationships with customers and create greater joint value. In return, suppliers can


June 4, 2009 19 of 52

financially benefit from the relationships due to frequent exchanges and low opportunism in

sharing values with customers. So, our fourth hypothesis is as follow:

Hypothesis 4a: As mutual dependence increases, the supplier-customer relationship

becomes closer through expanded information sharing and more joint decisions.

Hypothesis 4b: As mutual dependence increases, the supplier’s profitability from the

relationship with customer increases.

2.4 The Effects of Close Supplier-Customer Relationships

The literature views the ideal relationships between supplier and customer firms as cooperative

for mutual benefits. Cooperative activities such as information sharing and joint decisions can

bring the parties closer to each other and, in turn, are assumed to generate positive effects on

supplier firms’ financial performance. Suppliers can increase revenues by selling more of

existing products and/or by offering new products and services to the incumbent customers

through information sharing and/or joint decisions (Lyons et al. 1990; Kalwani and Narayandas

1995). Suppliers can also save money on the selling, administrative and general expenses by

servicing return customers (Kalwani and Narayandas 1995) and reduce inventory and

manufacturing costs by improving demand forecasts and manufacturability through information

sharing and/or joint decisions (Hall 1987; Frazier et al. 1988; Lyons et al. 1990; Kalwani and

Narayandas 1995; Hayes et al. 2005). Yet, close relationships will also increase the costs of

information sharing, activity coordination, and investments in transaction specific assets (Frazier

et al. 1988; Lyons et al. 1990). However, supplier firms are believed to benefit from close

relationships with customers because unless sufficient returns are provided, suppliers will

eventually disassociate themselves from cooperation and close relationships will not be

maintained (Kalwani and Narayandas 1995). Thus, our last hypothesis is as follow:

Hypothesis 5a: As the degree to which information sharing increases in a supplier-

customer relationship, the supplier’s profitability from the relationship improves.


June 4, 2009 20 of 52

Hypothesis 5b: As the degree to which joint decisions increase in a supplier-customer

relationship, the supplier’s profitability from the relationship improves.

The relationships referred to in the five hypotheses are illustrated in Figure 2.


June 4, 2009 21 of 52

3. METHODOLOGY

Questionnaire Development

The primary research vehicle for this study is a self-administered survey available both online

and by mail. Before developing the questionnaire, we conducted interviews with ten high-

ranking officers – holding titles as CEO, President and General Manager – at regional supplier

firms that met our sampling criteria (described below). Through these interviews, we confirmed

the existence of the phenomenon under study, the relevance of the research to practice, and the

feasibility of our data collection method (e.g., the position and number of informants). Based on

the literature and these interviews, the questionnaire was developed and then reviewed for face

and content validity by four professors in operations management. In connection with that, the

item-sorting technique was used to identify items that should be grouped into constructs. Before

being deployed nationwide, the questionnaire was pre-tested by 12 professionals, including those

interviewed, and verified for the appropriateness of the terminology used, the clarity of the

instructions, and the response formats.

Measures

The key constructs in our conceptual framework (see Figure 2) are operationalized by using

multi-item scales that can be assessed for reliability and construct validity (Malhotra and Grover

1998). The primary data collected through the survey include the supplier’s practices

implemented to achieve desirable operations capabilities, the supplier’s view of bi-lateral

dependence (the supplier’s perception of its dependence on a customer and, in turn, how this

customer may view its dependence on the respondent’s firm), the supplier-customer relationship

(information sharing and joint decisions), and the performance of the particular relationship (see

Appendix A and B for items).

Operations Capabilities

A company’s capabilities are measured as a group of associated practices and processes. Since

this is a relatively new approach to measure capabilities, we created 8 new scale items and

modified 20 existing items that were used in previous studies such as Flynn, Schroeder and

Sakakibara (1994), Ward and Duray (2000), Flynn and Flynn (2004), Pagell and Krause (Pagell


June 4, 2009 22 of 52

and Krause 2004) and Ittner and Larcker (Ittner and Larcker 1997b). Through the item-sorting

exercise, it was suggested that certain practices and processes are related to more than one

capability. Therefore, the association of individual practices and processes with a capability is

not specified a priori and subject to exploratory analysis.

Conformance quality is expected to capture a supplier’s ability to make products with consistent

quality that meets established performance standards. Cost efficiency is expected to capture the

supplier’s ability to minimize production costs through efficient operations, process technology,

and/or scale economies. Delivery dependability is expected to capture the supplier’s ability to

response to customer demand with speed and accuracy. Flexible responsiveness is expected to

capture the supplier’s ability to modify products and associated processes for non-routine

demands. New product development is expected to capture a supplier’s ability to reduce sources

of variations in new products with enhanced styling, features and performance in the

development process in order to improve manufacturability. New product introduction is

expected to capture the ability to rapidly introduce new products into full scale production.

Dependence

The bi-lateral dependence of a supplier and a customer was measured by the supplier’s

perception in our study for a couple of reasons. According to Kim, Pinkley and Fragale’s power

dynamics model (2005), power can be decoupled into four interactive components: potential

power, perceived power, power tactics and realized power. Potential power refers to a party’s

underlying capacity to obtain benefits from the other party while realized power refers to the

extent to which a party has claimed benefits from the interaction. Kim et al. (2005) argue that the

realization of the potential power depends on the parties’ perceptions of the potential power (i.e.,

the perceived power), their efforts to change these perceptions (i.e., the power-change tactics),

and the manner and extent to which the parties attempt to claim benefits from the interaction (i.e.,

the power-use tactics). Specifically, it is a party’s assessment of the potential power in the

relationship that drives his/her tactical decisions. These, in turn, can influence the parties’

interdependency and mediate the link between potential and realized power. Thus, this suggests

that the supplier’s perception of potential power – of his/her own potential power as well as that

of his/her customer’s – will affect the supplier’s mutual dependence and power imbalance.


June 4, 2009 23 of 52

Furthermore, previous studies show that the response rate can be greatly diminished by

collecting paired responses from two different organizations (i.e., supplier and customer firms)

(Johnston et al. 2004; Wathne and Heide 2004). In their study of the relationship governance in a

supplier chain network, for instance, Wathne and Heide (2004) used paired responses and ended

up with a sample size of 81, the lower of the two response sizes (retailers = 81 and focal

companies = 421). Thus, for both conceptual and practical reasons, we relied on the supplier’s

perception of power/dependence to measure mutual dependence and power imbalance in a

relationship.

The dependence measure describes the extent to which exchange partners depend on each other

and is expressed by the ease with which an individual exchange partner could be replaced. In

specific, the supplier’s dependence on the customer is measured by his/her ability to replace the

customer and the customer’s dependence on the supplier is measured by the supplier’s

perception of his/her customer’s ability to replace the supplier. The scale items are based on

Heide (1994) and Kumar, Scheer and Steeenkamp (1995).

Supplier-customer relationship

This measure describes the extent to which a supplier-customer relationship is cooperative for

mutual benefits. Information sharing refers to the degree to which each party discloses

information that may facilitate the other party’s activities. Joint decisions refer to the extent to

which the parties undertake activities jointly rather than unilaterally. The scale items are adopted

from Heide and Miner (1992) and Johnston, McCutcheon, Stuart, and Kerwood (2004).

Control measures

The control measures include industry, firm size, holding status, percentage of a supplier’s sales

accounted by a customer, market uncertainty, and the length of the relationship. These measures

are included to control for extraneous effects on the supplier’s perception of dependency, the

supplier-customer relationships, and the supplier’s financial performance. Industry membership

is measured by primary SIC code recorded in the OneSource database. Firm size is measured by

sales revenue (Ghosh and John 2005). Holding status measures the main holder of a company


June 4, 2009 24 of 52

and suggested during interviews with local companies. A percentage of a supplier’s sales

accounted by a customer measures a percentage of the supplier’s sales going to the chosen

customer (Ghosh and John 2005). Market uncertainty measures the predictability of the market

in which the supplier serves the chosen customer (Anand and Ward 2004). The length of the

relationship measures the number of years that the supplier has conducted business with the

customer (Ghosh and John 2005).

Sample Selection

Our sampling frame encompasses firms that meet the following criteria: (1) manufacturing firms

operating in 34-38 SIC code range, (2) firms that are suppliers to manufacturers, and (3) firms

with an employee bases in the range from 100 to 1,000. These sampling criteria are chosen for

the following reasons. Firstly, there is anecdotal evidence that the close relationship paradigm is

widely accepted among OEMs in manufacturing industries, especially in the 34-38 SIC range

(Lyons et al. 1990; Kalwani and Narayandas 1995), while the actual supplier-manufacturer

relationships vary substantially with respect to their closeness (Heide and John 1990; Ghosh and

John 2005). Secondly, anecdotal evidence indicates that many parts and material suppliers in

these industries have adopted new management philosophies, such as JIT and lean

manufacturing, but are purportedly not always rewarded for their initiatives due to high customer

concentrations in their markets (i.e., the buyers have high power relative to the suppliers;

(Balakrishnan et al. 1996). Lastly, manufacturing firms in these SIC code brackets operate in

diverse business environments which suggest substantial variations in the firms’ operations

strategies (i.e., cost reduction vs. innovation) within and across industries.

Using a commercial database (One Source; www.onesource.com) we identified U.S. firms with

an employee base between 100 and 1,000 and a SIC code in the 34-38 range, and created a list of

high-ranking general managers as well as managers in operations and related areas. Typical titles

include CEO, President, VP of manufacturing/supply chain/operations, and similar. In the second

step, we contacted all the identified firms by phone (1) to confirm whether the company is a

manufacturer that supplies primarily to industrial customers excluding dealers, resellers and

individual consumers, and (2) to identify and recruit a key informant who possesses necessary

knowledge about the phenomena under the study and is willing to participate in the survey. A


June 4, 2009 25 of 52

total of 3,107 firms were contacted in this way over a seven-week period. Of these, 1,773 firms

were ruled out because they were no longer available at the listed phone numbers, did not meet

our sampling criteria, or were unwilling to participate. Of the remaining 1,334 firms, 255 firms

that met our sampling criteria agreed to participate in the study while 1,079 firms were not

reached by phone but were still considered potential respondents.

The survey instrument and an instruction letter were sent to the 1,334 firms either via e-mail or

mail depending on the availability of contact information. To ensure the eligibility for this study,

we included a screening question in our questionnaire. In addition, each informant was requested

to self-report on a 7-point Likert-type scale his/her degree of knowledge in three key topic areas

– operations, customer relationships, and performance – to assure the reliability of responses.

The informants were asked to complete the questionnaire with respect to one of the company’s

four biggest customers in terms of sales volume. It is believed that informants can recall more

details about salient relationships than about non-salient ones, thus increasing the accuracy of the

perceptual measures, especially for financial metrics (Heide and John 1988). Three to five

follow-up messages were sent over a twelve-week period (extended time due to holiday season)

to verify the receipt of the survey and to remind the subjects to respond to the survey. We

received 118 responses from the 255 firms that originally agreed to participate (a response rate of

46.3%) and 77 responses from the 1,079 firms that were not previously contacted by phone (a

response rate of 7.1%). In sum, we received 195 responses for an overall response rate of 14.6%.

Of these, we excluded 46 responses due to various reasons. Thirty-six responses did not meet our

definition of suppliers, one survey form had more than two-thirds of missing data, and eight

responses put the informant’s self-rating of his/her knowledge of one of more of the three survey

topics below “fair” (< 5 on a 7-point scale). Our final usable sample size is 158, including nine

responses from the pre-test.

4. DATA ANALYSIS

4.1 Evaluating Bias

Coverage of the sampling frame was evaluated by comparing the profile of firms in the sampling

frame to the population of U.S. firms operating in the same SIC code range in terms of number

of employees and annual sales revenue. There were 308 firms that met our sampling criteria. Of


June 4, 2009 26 of 52

these, 255 firms were screened by phone prior to the survey, while 53 firms were not reached by

phone but their eligibility was confirmed by the returned questionnaires. According to the 1997

Economic Census broken down by SIC code, our sampling frame includes larger firms than the

average manufacturing firms in U.S. in term of annual sales and the number of employees (Table

1). When compared to firms in the manufacturing industry with more than 100 employees,

however, our sampling frame includes smaller firms in terms of number of employees.

According to the 2006 Economic Census by sector, firms with more than 100 employees hired

553 employees on average (www.census.gov).

Table 1: Demographic of Firms in the Sampling Frame

1997 Sampling Frame SIC Description % Average

Sales (mill) Average

Employees % Average

Sales (mill) Average

Employees 34 Fabricated metal products 28.02 6 41 22.1 99.17 267

35 Industrial machinery and equipment

41.58 11 52 38.3 96.28 278

36 Electronic and other electric equipment

12.61 9 42 22.1 64.97 251

37 Transportation equipment 9.14 14 41 6.2 180.83 352 38 Instruments and related

products 8.65 4 3 11.4 102.36 228

34-38 Manufacturing 100 12 50 100 94.4 268 Source: U.S. Census Bureau (www.census.gov)

Non-response bias was assessed by comparing responding firms with non-responding firms

matching our sampling frame on key demographic characteristics such as SIC, number of

employees, and annual sales revenues (data collected from OneSource; www.onesource.com).

The chi-square test on the group differences based on SIC suggested no significant difference

between the two groups (chi-square = 2.447, d.f. = 4, p-value = 0.654). The t-test was performed

to investigate the mean differences in number of employees and annual sales revenues between

the two groups. The results suggested no significant differences in neither number of employees

(t = 0.174, d.f. = 257, p-value = 0.862), nor in annual sales revenues (t = -0.509, d.f. = 285, p-

value = 0.611).

Two types of response bias were assessed based on the response methods (i.e., online responses

vs. mail responses) and the responding time (early responses vs. late responses). The t-tests were


June 4, 2009 27 of 52

performed to investigate the mean differences in responses on 51 scale items related to the

research constructs between online and mail responses and between early and late responses,

respectively. The results suggested no significant differences at alpha of 0.05 for over 90% of

those tems (significant differences for four items between online and mail responses and for

three items between early and late responses, respectively).

Missing value analysis was performed by Little’s MCAR test (SPSS 16.0) to evaluate if missing

values are randomly distributed across all observations. The test result suggested that values are

missing completely at random across observations (chi-square = 1418.113, d.f. = 1368, p-value =

0.169). The missing values were replaced by means in the further analysis.

4.2 Measurement Model

As discussed earlier, operations strategy is defined as a pattern of decisions regarding the

selection and development of capabilities – with the latter accomplished through a variety of

strategic choices of operational practices and processes. To measure the capabilities-based

concept of operations strategy in this relatively new perspective (Peng et al. 2008), we created 8

new items and modified 20 existing items that were used in previous studies. For content validity,

the 28 items were reviewed by four academic experts who associated each item with a specific

capability. Through the item-sorting exercise, it was suggested that certain practices and

processes are related to more than one capability. Therefore, we performed an exploratory factor

analysis to identify the dimensional structure of capabilities – the number of distinctive

capabilities and the uniquely associated practices with each capability – prior to a confirmatory

factor analysis.

It would have been ideal to identify the dimensional structure of the capability measures through

exploratory data analysis in a pre-test if the sample size allowed. Instead, we randomly split the

total final sample (n = 158) into equal halves (n = 79) to be used for calibration and validation,

respectively. In the first stage, we used the calibration sample to define a measurement model

through exploratory and confirmatory analysis. In the second stage, we cross-validated the

measurement model – specified a priori – with the validation sample to evaluate the

generalizability of the measures (Anderson and Gerbing 1988; Netemeyer et al. 2003). In the last


June 4, 2009 28 of 52

stage, we estimated validity, reliability and unidimensionality of all measures for the whole

sample. Although we estimated validity and reliability of all measures separately for each of the

calibration, validation and whole samples, we only report the results for the whole sample to

avoid redundancy except for the capability measures because they include new scale items.

Calibration

Measurement calibration was conducted as follows. First, a Corrected Item to Total Correlation

(CITC) was computed for each item to evaluate item reliability. Three items with CITC values

below 0.30 were removed (SHP, GEQ and ROUT). Second, an exploratory factor analysis (EFA)

was performed for the 25 remaining items to identify the underlying structure of capabilities. The

analysis was conducted with CF-Varimax oblique rotation and the maximum likelihood

estimation (MLE) method using M-plus 5.0 (Muthén and Muthén 2007). The analysis was

iterated with two to seven factors in multiple steps while considering Eigen values greater than 1,

RMSEA, and a 90% confidence interval associated with RMSEA, among other fit indices, to

determine the appropriate number of factors (Browne and Cudeck 1992). From this iterative

analysis, a 6-factor solution was chosen based on interpretability and goodness of fit. Three items

(UTL, STU and LT) had mediocre loadings across two factors and four items (STAT, LAY, JOB

and CTN) had substantial cross-loadings on two factors (loading > |0.3|). EFA was repeated after

removing the three items with mediocre loadings. The 6-factor model maintained the same

dimensional structure as before and still looked best in terms of interpretability and goodness of

fit. As recommended by Netemeyer et al. (2003), the four items with cross-loadings were kept

for the confirmatory factory analysis (CFA) to evaluate their dimensionality.

A confirmatory factor analysis (CFA) was performed to evaluate the adequacy of the

dimensional structure extracted during EFA and to assess the construct validity of the

measurement model. The model is identified because the model has more than one factor and

each factor has at least two indicators (Bollen 1989). Given our calibration sample size of 79, the

6-factor model with 22 items can achieve a statistical power of 0.75 with 194 degrees of freedom

at alpha of 0.05 (MacCallum et al. 1996). The analysis was conducted by LISREL 8.80 with a

covariance matrix extracted from raw data and MLE. In the first iteration, an analysis of the fit

indices, the standardized residuals and the modification indices suggested a violation of the


June 4, 2009 29 of 52

unidimensionality of six items (STAT, LAY, JOB, CTN, STD and SIN). The overall fit indices

were outside the acceptable range. These manifest variables had large standardized residuals (in

absolute values) with several manifest variables of other factors (> |2.58|) and large modification

indices associated with them (>10). In subsequent iterations, we eliminated one manifest variable

at a time to determine whether or not to keep these variables. As the result, we decided to drop

all six items. The result of the last iteration suggested convergent validity, item-level reliability

and unidimensionality for the remaining capability measures. The factor loadings for each of the

16 items are statistically significant with t-values exceeding 3.64 (p < 0.001) and the variance of

the observed variables explained ranges from 0.28 to 0.88 (Table 2). All the fit indices are within

acceptable ranges (Table 3).

The measures for dependence (the supplier’s dependence and the customer’s dependence),

supplier-customer relationship (information sharing and joint decisions) and performance were

also evaluated separately and calibrated for validity and reliability. As the result, two items were

removed from the dependence measures (one item each from the supplier’s dependence and the

customer’s dependence) and two items were removed from the performance measures. The

retained scale items are reported in Appendix A and the ones removed in the calibration process

are reported in Appendix B.

Cross-Validation

The measurement model specified in the calibration process was retested with the validation

sample. The factor loadings for each of the 16 items are statistically significant (Table 2) and the

overall fit indices are within acceptable ranges (Table 3). Evaluation of other measures also

suggests convergent validity, item-level reliability and unidimensionality. The similar pattern

and size of factor loadings in calibration and validation samples suggest that the measurement

model has invariance of form across samples.


June 4, 2009 30 of 52

Table 2: Capability Measures for the Calibration, Validation and Whole Samples

Item LV Calibration sample (n = 79)

Validation sample (n = 79)

Whole sample (n = 158)

λi (t-value)a R2 λi (t-value) a R2 λi (t-value) a R2

HKP ← QUAL 0.76 (6.92) 0.58 0.71 (6.12) 0.51 0.72 (8.05) 0.51 MTN ← QUAL 0.94 (8.70) 0.88 0.76 (6.47) 0.57 0.73 (8.18) 0.53 JIT ← EFF 0.68 (6.02) 0.46 0.62 (5.24) 0.38 0.68 (8.14) 0.46 VSM ← EFF 0.76 (6.95) 0.58 0.66 (5.59) 0.43 0.61 (7.22) 0.37 PUL ← EFF 0.53 (4.50) 0.28 0.63 (5.37) 0.40 0.65 (7.78) 0.43 SLT ← EFF 0.64 (5.57) 0.40 0.45 (3.65) 0.20 0.49 (5.62) 0.24 VI ← DEL 0.70 (5.47) 0.49 0.32 (2.17) 0.10 0.54 (5.10) 0.30 OTD ← DEL 0.61 (4.92) 0.37 0.89 (3.10) 0.78 0.59 (5.33) 0.35 ENT ← FLEX 0.87 (4.35) 0.76 0.76 (5.78) 0.58 0.63 (5.72) 0.40 NPM ← FLEX 0.57 (3.65) 0.33 0.73 (5.60) 0.53 0.72 (6.13) 0.52 ENI ← NPI 0.75 (6.72) 0.57 0.84 (8.23) 0.71 0.79 (9.64) 0.62 CFT ← NPI 0.83 (7.49) 0.69 0.88 (8.70) 0.77 0.83 (10.18) 0.69 EZMF ← NPI 0.56 (4.87) 0.32 0.56 (5.04) 0.31 0.45 (5.39) 0.20 DOE ← NPD 0.71 (6.34) 0.50 0.74 (6.80) 0.54 0.77 (10.01) 0.59 QFD ← NPD 0.65 (5.72) 0.42 0.67 (6.06) 0.45 0.75 (9.74) 0.57 FMEA ← NPD 0.79 (7.28) 0.63 0.83 (7.86) 0.69 0.74 (9.59) 0.55 aStandardized factor loading (t-value).

Table 3: Measures of Fit for Capability Measures with Calibration, Validation and Whole Samples

Measures of fit Calibration sample (n = 79)

Validation sample (n = 79)

Whole sample (n = 158)

Recommended value for close or acceptable fit

χ2 –Test statistic (d.f.) 102.12 (89) 113.19(89) 135.11 (89) NA Root mean square error of approximation (RMSEA)

0.044 0.059 0.057 ≤ 0.08

RMSEA, 90% confidence interval (0.0; 0.078) (0.012; 0.089) (0.036; 0.076) (0.00; 0.08) P value H0: closer fit (RMSEA ≤ 0.05) 0.59 0.32 0.26 ≥ 0.05 Standardized root mean square residual (RMR) 0.070 0.082 0.056 ≤ 0.10 Non-normed fit index (NNFI) 0.94 0.90 0.92 ≥ 0.90 Parsimony normed fit index (PNFI) 0.62 0.58 0.63 ≥ 0.70 Comparative fit index (CFI) 0.96 0.92 0.94 ≥ 0.90Incremental fit index (IFI) 0.96 0.92 0.94 ≥ 0.90Normed χ2 (χ2/d.f.) 1.15 1.27 1.52 ≤ 0.30

Validity and Reliability of the Measurement Model

Convergent validity is believed to exist if the factor loading of a manifest variable on its

respective latent variable is statistically significant (Anderson and Gerbing 1988). Accordingly,

we tested convergent validity of all measures by evaluating the factor loadings of each item on

its intended construct. The factor loadings for each of the 34 items are statistically significant

with t-values exceeding 5.1 (p < 0.0001). Results for the convergent validity assessment of all

measures are reported in Table 4.


June 4, 2009 31 of 52

Reliability is assessed in three ways: by Cronbach’s Alpha, composite reliability, and average

variance extracted (AVE) (Bollen 1989). Cronbach’s Alpha exceeded the threshold value of 0.60

for 10 of 11 latent variables and composite reliability exceeded the recommended value of 0.70

for 8 out of 11 latent variables. AVE exceeded the recommended value of 0.50 for 6 out of 11

factors. Since the reliability measures are largely influenced by the number of items, the latent

variables with two indicators (i.e., QUAL, DEL, and FLEX) showed lower reliability than those

latent variables with three or more indicators. However, only DEL failed to exceed the reliability

threshold values for all three tests. Therefore, we eliminated DEL in our structural model.

Table 4: Validity and Reliability of the Measurement Model for the Whole Sample (n = 158)

Item LV λi (t-value) a R2 Cronbach’s α

Composite reliability

AVE

HKP ← QUAL 0.72 (8.05) 0.51 0.68 0.69 0.52 MTN ← QUAL 0.73 (8.18) 0.53 JIT ← EFF 0.68 (8.14) 0.46 0.69 0.70 0.42 VSM ← EFF 0.61 (7.22) 0.37 PUL ← EFF 0.65 (7.78) 0.43 SLT ← EFF 0.49 (5.62) 0.24 VI ← DEL 0.54 (5.10) 0.30 0.46 0.49 0.33 OTD ← DEL 0.59 (5.33) 0.35 ENT ← FLEX 0.63 (5.72) 0.40 0.62 0.63 0.46 NPM ← FLEX 0.72 (6.13) 0.52 ENI ← NPI 0.79 (9.64) 0.62 0.72 0.74 0.50 CFT ← NPI 0.83 (10.18) 0.69 EZMF ← NPI 0.45 (5.39) 0.20 DOE ← NPD 0.77 (10.01) 0.59 0.80 0.80 0.57 QFD ← NPD 0.75 (9.74) 0.57 FMEA ← NPD 0.74 (9.59) 0.55 SDP1 ← SDP 0.66 (7.44) 0.43 0.68 0.72 0.46 SDP2 ← SDP 0.62 (7.05) 0.38 SDP3 ← SDP 0.76 (8.35) 0.58 CDP1 ← CDP 0.65 (7.74) 0.42 0.75 0.76 0.52 CDP2 ← CDP 0.63 (7.57) 0.40 CDP3 ← CDP 0.89 (10.05) 0.75 INF1 ← INF 0.76 (10.54) 0.58 0.84 0.84 0.58 INF2 ← INF 0.81 (11.40) 0.65 INF3 ← INF 0.67 (8.86) 0.45 INF4 ← INF 0.79 (11.16) 0.63 JD1 ← JD 0.52 (6.41) 0.27 0.79 0.80 0.45 JD2 ← JD 0.68 (8.80) 0.46 JD3 ← JD 0.67 (8.68) 0.45 JD4 ← JD 0.80 (10.88) 0.63 JD5 ← JD 0.65 (8.36) 0.42 GM ← PERF 0.54 (7.06) 0.29 0.82 0.84 0.65 RV ← PERF 0.98 (14.73) 0.96 SG ← PERF 0.84 (12.01) 0.71 aStandardized factor loading (t-value).


June 4, 2009 32 of 52

Unidimensionality of measures is assessed by the model fit and the standardized residuals

(Anderson and Gerbing 1988; Hu and Bentler 1995; Netemeyer et al. 2003). The overall fit

indices suggest that the a priori specification of the measurement model is a good fit to the data

(chi-square = 588.55, d.f. = 472, CFI = 0.92, NNFI = 0.91, RMSEA = 0.04, Standardized RMR =

0.065). Hu and Bentler (1995) recommend that no more than 10% of standardized residuals in

absolute value are greater than |2.58|. The proportion of absolute standardized residuals greater

than this threshold value was 6.92% for all measures (16 out of 231), suggesting the

unidimensionality of items and a good fit of the measurement model (Anderson and Gerbing

1988; Hu and Bentler 1995) .

Discriminant validity is assessed by constraining a correlation of two latent variables to 1 and

comparing the chi-square difference between the constrained and unconstrained models for all

possible pairs of latent variables. This test is performed for one pair of factors at a time as

suggested by Anderson and Gerbing (1988). As reported in Table 5, the chi-square difference

tests suggest discriminant validity of all latent constructs.

Table 5: Correlations and Discriminant Validity for Whole Sample (n = 158)

LV 1 2 3 4 5 6 7 8 9 10 11 1 QUAL 27.50 6.56 27.86 36.19 44.50 41.75 42.31 47.99 38.27 42.82 2 EFF 0.54** 12.75 26.70 97.49 109.34 128.03 116.88 126.12 119.40 179.25 3 DEL 0.57** 0.41** 15.73 14.95 14.26 18.04 15.52 16.67 17.99 14.75 4 FLEX 0.34** 0.27* 0.19 26.61 35.80 32.98 31.33 28.24 24.82 30.91 5 NPI 0.39** 0.38** 0.24 0.35** 103.82 100.30 108.41 98.27 97.66 99.48 6 NPD 0.10 0.31** 0.38** -0.14 0.20* 96.53 121.09 148.33 153.67 172.18 7 SDP -0.08 0.02 -0.02 -0.27* 0.06 0.14 95.08 88.40 98.31 94.04 8 CDP 0.04 -0.03 0.13 0.15 -0.005 0.16 0.16 125.31 127.69 119.93 9 INF 0.27** 0.04 0.23 0.33** 0.09 -0.001 0.20* 0.03 130.52 156.27

10 JD 0.33** 0.14 0.29* 0.33** 0.29** 0.16 0.19 0.10 0.63** 177.24 11 PERF -0.05 0.008 0.06 0.29** 0.01 -0.07 -0.30** -0.03 0.30** 0.16 The lower triangle shows correlations with * p < 0.05 and ** p < 0.01 (two-tailed t-test). The upper triangle shows the difference in χ2-test statistic between the constrained and unconstrained CFA models; all χ2 differences are significant at p < 0.01.

Common method bias

Harmon’s single-factor test and the marker-variable technique were used to estimate common

method variances (CMV) caused by the common method (i.e., self-reported questionnaire by a

single informant at one point in time) used in our data collection (Lindell and Whitney 2001;

Malhotra et al. 2006). We used CFA, as an alternative to EFA, to conduct Harmon’s single-


June 4, 2009 33 of 52

factor test by modeling all manifest variables as the indicators of a single-factor – a common

method factor. If a single-factor model fits the data, common method bias is assumed to be

substantial (Malhotra et al. 2006). The result of Harmon’s single-factor test suggested no

evidence of common method bias of any substantial size (chi-square = 1922.70, d.f. = 527, CFI =

0.53, NNFI = 0.50, RMSEA = 0.130, Standardized RMR = 0.347).

Since Harmon’s single-factor test is known for its insensitivity in detecting common method

biases of moderate or small sizes, we took extra precautions to assess CMV by using a marker-

variable technique in addition to the single-factor test (Lindell and Whitney 2001; Malhotra et al.

2006). A marker variable is a special variable that is theoretically unrelated to the variables of

research interests but included in a study to detect CMV. Because the marker variable is assumed

to have no theoretical relationships with the variables in the study, Lindell and Whitney (2001)

argue that CMV can be estimated based on correlations between the marker variable and the

theoretically unrelated variables. To estimate CMV by this method, we adopted a marker-

variable from Malhotra et al. (2006), which measures the degree of one’s vivid imagination, and

included it in our questionnaire. We believed that this variable is not theoretically related to any

of main constructs of our study.

CMV is estimated by using the marker-variable technique as follow. Firstly, our measurement

model is retested with the marker-variable included. The results suggest a good fit of the model

to the data (chi-square = 665.37, d.f. = 528, CFI = 0.91, NNFI = 0.90, RMSEA = 0.041,

Standardized RMR = 0.065). Secondly, the correlations between the marker-variable and other

variables are examined. According to Lindell and Whitney (2001), the second smallest positive

correlation is selected as a conservative estimate of CMV. The size of correlations ranges from -

0.11 to +0.08 and the second smallest positive correlation is 0.004. Thirdly, we adjusted all the

correlations for CMV and tested their significance by using Equation 1 and 2 in Appendix C,

respectively. If “any correlations that were statistically significant remain significant” after

adjusted for CMV, common method bias can be assumed to be unaccountable for the results

(Lindell and Whitney 2001, p. 118). As shown in Table 5, 23 correlations were statistically

significant at p < 0.05. After adjusted for CMV, all of them remained statistically significant.

Thus, common method bias cannot be accountable for the correlation results. Lastly, to be


June 4, 2009 34 of 52

conservative we performed a sensitivity analysis by using various estimates of CMV – the third

smallest through the largest positive correlations between the marker variable and other variables

as suggested by Lindell and Whiteney (2001). The results remained the same regardless of the

selection of the CMV estimate. Hence, common method bias, although it exists, should not be a

problem for further analysis.

4.3 Structural Model

Since the structural model consists of multi-dimensional layers, the constructs in each layer are

introduced into the model in a stepwise addition. In the first step, the paths from the six

capabilities (QUAL, EFF, DEL, FLEX, NPI and NPD) to mutual dependence (MD) and power

imbalance (PI) were structured. Out of 12 paths, only six paths from three capabilities (EFF,

FLEX and NPD) to the dependences (MD and PI) were statistically significant. In the second

step, the paths from the dependence constructs (MD and PI) to the relationship constructs (INF

and JD) were added. In the third step, the paths from the dependence and the relationship

constructs (MD, PI, INF and JD) to the performance construct (PERF) were added. In the last

step, the control variables (i.e., industry, firm size, holding status, percentage of a supplier’s sales

going to the chosen key customer, market uncertainty, and length of the relationship) were added

one at a time and evaluated for the significance of their effect on the main constructs. The results

showed that all of these variables except for one, the percentage of a supplier’s sales accounted

by a customer, should be controlled for their significant relationships with either the dependence

(MD and PI) or the relationship constructs (INF and JD). In the final model, we included only

those variables that have at least one significant path in order to reduce the number of parameters

to estimate and to improve the statistical power. The power of the final model is near 1 based on

the power analysis method of MacCallum et al. (1996). The results are reported in the next

section.

5. RESULTS

Overall, the goodness of fit indicates that our conceptual framework is supported by the data

(Figure 3). As for the hypothesis testing, the results are mixed. In H1, we expected the

capabilities – either associated with cost reduction or innovation strategy – to be positively


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associated with MD while negatively associated with PI. The results show that FLEX and NPD

are negatively associated with PI, EFF is positively associated with PI, and NPD has a positive

association with MD. This suggests that the degree of a supplier’s implementation of operations

capabilities increases MD and reduces PI as expected. Thus, H1 is partly supported.

Cost Efficiency (EFF)

Flexible Responsiveness

(FLEX)

New Product Development

(NPD)

Supplier’s Financial Performance from

Relationship (PERF) (R2=0.15)

Information Sharing (INF)

(R2=0.10)

Joint Decisions (JD) (R2=0.20)

Mutual Dependence

(MD)(R2=0.09)

Power Imbalance (PI) (R2=0.17)

H1 & H2

H4a

H3a

H4b: -0.23**

Covariates: Industry and the Length of Relationship

Covariates: Firm Size, Market Uncertainty and

Holding

-0.11

0.25**

-0.16*

0.07

-0.41***

0.04

0.16*

-0.21*

0.21**

0.18*

H5a: 0.34***

H5b: 0.13

Industry: 0.14*

Length: 0.20**Uncertainty: 0.20*

Holding: -0.15**

Size: 0.17*

Uncertainty: 0.26**

H3b: -0.12

Chi-square = 498.71/ d.f. = 387, CFI = 0.92, NNFI = 0.90, RMSEA = 0.043, Standardized RMR = 0.089Standardized estimates are reported with *p < 0.05, **p < 0.01, ***p < 0.001 (one-tailed)

Significant paths Insignificant paths

Figure 3: Structural Model

In H2, we hypothesized differential effects on MD and PI depending on the associated strategy.

For instance, we expected the capabilities associated with an innovation strategy to decrease PI

(or increase MD) to a greater degree than the capabilities associated with a cost reduction

strategy. The results show that the capabilities associated with an innovation strategy (FLEX and

NPD) decrease PI while the capability associated with a cost reduction strategy (EFF) increases

it. NPD also increases MD while EFF does not. This strongly suggests that capabilities


June 4, 2009 36 of 52

associated with an innovation strategy have more favorable impacts on a supplier’s PI than

capabilities associated with a cost reduction strategy. Thus, H2 is supported.

In H3a, we expected PI to be negatively associated with INF and JD. The results show that PI

has a significant negative association with JD while having no significant association with INF.

It suggests that as a supplier’s PI reduces, the supplier-customer relationship becomes closer

through increased JD. Thus, H3a is partly supported.

In H3b, we expected a negative relationship between PI and PERF. The results show that PI has

a negative but insignificant association with PERF. It suggests that a reduction in a supplier’s PI

does not lead to the improvement of the supplier’s profitability from this customer relationship.

Thus, H3b is not supported.

In H4a, MD was expected to be positively associated with INF and JD. The results show that

MD is positively associated with both variables. This suggests that as MD increases the supplier-

customer relationships become closer through expanded information sharing and more joint

decisions. Thus, H4a is supported.

In H4b, we expected a positive relationship between MD and PERF. Surprisingly, the results

show that MD is negatively associated with PERF. It suggests that as MD increases the

supplier’s profitability decreases rather than increases. Thus, H4b is not supported.

In H5a and H5b, we expected INF and JD to be positively associated with PERF. The results

show that INF has a positive significant association with PERF while JD has a positive but

insignificant association with PERF. Thus, H5a is supported while H5b is not.

The results for the control variables are as follow. MD is positively associated with industry and

the length of a relationship. INF is positively associated with market uncertainty while negatively

associated with the holding status of a firm. JD is positively associated with market uncertainty

and the size of a firm.


June 4, 2009 37 of 52

In addition, we performed a post hoc analysis of the model with SDP and CDP (in the place of

MD and PI in our conceptual framework) to better understand the results of the hypothesis tests.

Cost Efficiency (EFF)

Flexible Responsiveness

(FLEX)

Supplier’s Financial Performance from

Relationship (PERF) (R2=0.22)

Information Sharing (INF)

(R2=0.15)

Joint Decisions (JD) (R2=0.20)

Supplier’s Dependence

(SDP)(R2=0.109)

Customer’s Dependence

(CDP) (R2=0.105)

-0.40***

Covariates: Industry and the Length of Relationship

Covariates: Firm Size, Market Uncertainty and

Holding

0.05

-0.18(p=0.078)

0.09

-0.02

0.24*

-0.21*

0.08

0.22*

0.23*

0.29**

0.38***

0.05

Length: 0.20*Uncertainty: 0.22*

Holding: -0.16*

Uncertainty: 0.30**

0.01

Chi-square = 654.34/ d.f. = 512, CFI = 0.91, NNFI = 0.90, RMSEA = 0.042, Standardized RMR = 0.086Standardized estimates are reported with *p < 0.05, **p < 0.01, ***p < 0.001 (one-tailed)

Significant paths Insignificant paths

Figure 4: Post Hoc Analysis

Length: 0.15*

New Product Development

(NPD)

The results of the post hoc analysis provide a couple of important insights (Figure 4). First, it

offers a possible explanation for the differential effects of EFF, FLEX and NPD on MD and PI.

Recall that PI = SDP/CDP and MD = SDP x CDP. This implies that a supplier’s PI can decrease

as CDP increases or SDP decreases. It also implies that MD can increase as either one, or both,

of CDP and SDP increases. The results show that FLEX and NPD are positively associated with

CDP while only FLEX has a negative association with SDP. On other hand, EFF has a negative,

although weakly significant association (t=-1.42; p=0.078) with CDP. Given the definitions of PI

and MD, inferences can be made as follow. EFF increases a supplier’s PI by decreasing CDP


June 4, 2009 38 of 52

while leaving SDP intact. FLEX reduces a supplier’s PI but does not change MD because FLEX

increases CDP while simultaneously decreasing SDP almost to the same degree. NPD, on the

other hand, reduces a supplier’s PI and increase MD because it increases CDP while not

changing SDP. Thus, the analysis suggests that EFF, FLEX and NPD lead to the different levels

of MD and PI by affecting SDP and CDP to the different degrees.

Second, it also offers a possible explanation for why MD is negatively associated with PERF.

The results show that SDP, like MD, is positively associated with INF and JD while being

negatively associated with PERF. On the other hand, CDP is not associated with any of INF, JD

or PERF in spite of the similar levels of SDP (mean= 5.05; s.d.=1.02) and CDP (mean=4.47;

s.d.=1.20) observed in sample. Given the definition of MD, it can be inferred that SDP led MD

to have a negative association with PERF while CDP, via MD, did not affect PERF. Therefore,

the effect of MD on PERF is proportional to SDP but unrelated to CDP.

Furthermore, we estimated the direct, indirect and total effects of the variables separately (Table

6). This analysis offers a couple of noteworthy insights. First, the decomposition of total effects

into direct and indirect effects (total effect = direct + indirect effects) shows no direct effects of

the capabilities (EFF, FLEX and NPD) on INF, JD and PERF. It supports the current model

specification of no direct relationships between the capabilities and INF, JD and PERF. Second,

both MD and SDP have significant positive indirect effects (41.0% and 42.4% of the total effects,

respectively) on PERF through INF and JD. It suggests that the negative effects of MD and SDP

on PERF can be somewhat mitigated by cooperative interactions such as INF and JD.

Table 6: Direct (D), Indirect (I) and Total (T) Effects

LV INF JD PERF LV PERF EFF T -0.004 -0.065 -0.007 MD T -0.161

I -0.004 -0.065 -0.007 I 0.066

D 0 0 0 D -0.227 FLEX T -0.021 0.074 0.034 PI T -0.101 I -0.021 0.074 0.034 I 0.022 D 0 0 0 D -0.123 NPD T 0.015 0.068 -0.005 SDP T -0.283 I 0.015 0.068 -0.005 I 0.120 D 0 0 0 D -0.403 CDP T 0.011 I -0.002 D 0.013


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6. DISCUSSION

The main purpose of this study is to understand what a supplier firm can do to maintain or

enhance its profitability associated with a customer. Firstly, we investigated whether and how a

supplier’s strategic choices in the operations area affect its power/dependence relation with a

customer. It is often believed that a supplier’s excellence in any capability will enhance the value

of its products or services to a customer and, in turn, increase a customer’s dependence on a

supplier due to high switching costs. Unlike the conventional belief in the virtue of operational

capabilities of any kind, our findings suggest that not all supplier capabilities have a favorable

impact on the power/dependence relation with a customer. On the contrary, we found that

strategic capabilities have differential effects. For instance, cost efficiency tends to increase a

supplier’s power imbalance by reducing the customer’s dependence. This is probably because

cost efficiency is mainly achieved through internal practices without involving the customer in

the process or requiring the customer’s investment. Although a supplier with high cost efficiency

would offer cost benefits to a customer, it can be readily replaced by cheaper suppliers.

On the other hand, flexible responsiveness and new product development are likely to reduce a

supplier’s power imbalance by increasing a customer’s dependence because they require the

customer’s investment in TSAs to some or even great extent. Although both capabilities increase

a customer’s dependence, however, our findings suggest that flexible responsiveness, unlike new

product development, does not promote mutual dependence because it tends to reduce a

supplier’s dependence on the customer more than it increases the customer’s dependence on the

supplier. This is probably because a supplier achieves flexible responsiveness mainly through the

internal training of engineers and production workers. This type of investments in skills made by

a supplier can readily transfer outside a specific relationship while a customer’s investment in the

relationship is likely supplier-specific. Therefore, a supplier with high flexible responsiveness

might not be as dependent on the relationship as a customer would.

Secondly, we investigated whether and how the power-dependence is related to the suppliers’

profitability from the relationship. Surprisingly, our findings indicate that a stronger mutual

dependence hurts a supplier’s profitability while the level of power imbalance does not have an


June 4, 2009 40 of 52

impact. Specifically, this would suggest that a customer does not behave in accordance to a

power/dependence relation in dealing with a supplier. Given any level of dependence, for

instance, a customer in our sample does not appear to cooperate with a supplier for a close

relationship or a supplier’s profitability. On the other hand, a supplier appears to behave

according to its dependence on a customer because a supplier complies more with a customer as

its dependence increases.

It might be that a customer is used to realize its power against a supplier regardless of the power

position in the relationship while a supplier is generally hesitant to realize its power against a

customer – possibly due to fear of creating a greater disadvantage in the future (e.g., termination

of contract). Therefore, a supplier might be unwilling to avoid conflicts with a customer by

claiming greater share of values even if a supplier’s perceived relative power against a customer

increases. On the other hand, a customer might not voluntarily refrain from taking greater share

of the jointly created values from a supplier even in the presence of high mutual dependence.

Thus, our findings from a supplier’s perspective suggest that a customer and a supplier

demonstrate different behaviors in relationships depending on their role and that a supplier tends

to be taken advantage of by a customer regardless of its perceived power position in the

relationship due to high switching costs of a customer.

Lastly, we investigated whether and how power-dependence is related to the supplier-customer

relationships and, via those, to the supplier’s profitability. Our findings suggest that mutual

dependence leads to a close relationship through information sharing and joint decisions while a

supplier’s power imbalance impedes joint decisions. In turn, information sharing leads to the

improvement of a supplier’s profitability from a customer, while joint decision making has no

impact. It could be that a supplier benefits from information sharing by adding value to its

products or services for a price premium, and/or that information sharing with the customer

reduces the supplier’s costs. On the other hand, it might be that a customer dominates the

supplier in joint decisions and, thus, the decisions favor the customer more than the supplier.

Overall, our findings suggest that a supplier can improve its profitability from a relationship

through productive interactions like information sharing that can help a supplier create value for

both its customer and itself.


June 4, 2009 41 of 52

7. CONTRIBUTIONS

The theoretical contributions of our study are three-fold. First, we proposed a conceptual

framework that integrates a supplier’s operations strategy, the supplier-customer relationship and

the supplier’s financial performance from the relationship. Specifically, operations strategy –

viewed as a group of capabilities developed by the supplier – is introduced as an important

contributor in shaping the power/dependence relations between customers and suppliers. Our

findings of the differential effects of various capabilities on the power/dependence relations have

not been investigated in previous research, neither in the operations nor the marketing literature.

Thus, our study is a first inter-disciplinary attempt, to the best of our knowledge, to investigate

the interrelations between different functional decisions such as operations strategy and the

power/dependence relations between suppliers and customers.

Second, our study fills a gap in the supply chain literature by studying supplier-manufacturer

relationships from the suppliers’ perspective. This begins by mapping out the critical elements of

a relationship with a customer as seen by the supplier: the power/dependence structure (i.e.,

mutual dependence and power imbalance), the closeness of the relationship, and the supplier’s

profitability from the relationship. Our findings from the suppliers’ perspective show the

different behaviors of customers and suppliers in the relationships depending on their roles and

suggest the power/dependence – especially a supplier’s dependence on a customer – as an

important determinant of the supplier’s profitability from the relationship. Thus, our study

challenges a dominant assumption about role invariant perceptions and behaviors among supply

chain members and suggests a need to investigate key issues and problems considering that

separate perspectives are held depending on the role of the supply chain partner.

Third, the study assessed the economic benefits of “close” supplier-manufacturer relationships.

There is documented evidence that customer firms benefit from close relationships with suppliers

through reduced costs and increased profits (Noordewier et al. 1990; Cannon and Homburg

2001), but the benefits of close relationships accruing to supplier firms are not adequately

documented in the literature. Our study contributes to this void by studying the benefits of close

relationships accruing to supplier firms. In the study of the financial benefits of long-term


June 4, 2009 42 of 52

relationships, Kalwani and Narayandas (1995) found that suppliers in long term relationships

have higher sales growth at the expense of lower gross margins than the firms in transactional

relationships. Contrary to their findings, we found in this study that close relationships with

customers help suppliers improve sales revenues, gross margins and sales growth. Based on our

findings, it appears that mutual dependence increases over the length of a relationship and, in

turn, reduces the supplier’s overall profitability. Suppliers, however, appear to turn around the

negative effects of mutual dependence on its profitability and benefit from close relationships

through productive interactions like information sharing.

Our findings also have several important managerial implications. Firstly, our integrative study

suggests a need for suppliers to align operations capabilities with customer relationship of their

desire. Cost efficiency increases a supplier’s power imbalance in a relationship and, in turn,

reduces joint decisions. New product development capability, on the other hand, increases mutual

dependence while reducing a supplier’s power imbalance. High mutual dependence, in turn,

encourages expanded information sharing and more joint decisions between the parties. Thus,

suppliers pursuing close relationships with customers may need to improve new product

development capability.

Secondly, our study shows how power structures are related to the supplier-customer interactions

and the supplier’s financial performance. Managers at supplier firms need to proactively use such

integrative understanding of power, relationship and profitability to make tactical decisions with

respect to the manner and extent to which they claim benefits from the relationships (Kim et al.

2005). The most frequent complaints that suppliers express about their customers relate to

excessive price cuts which drive down the suppliers’ profit margins (Wilke 2004; Bunkley 2006).

Yet, supplier firms cannot walk away from their customers without significant economic losses.

In such lock-in situations, the dependency between suppliers and customers can play an

important role in determining a supplier’s ability to negotiate for its fair share of the value

created by a relationship. However, our findings from a supplier’s perspective suggest that

suppliers rarely leverage their power to improve profitability even though they successfully

improve their customers’ dependence by providing flexible responsiveness and new product

development capabilities. If suppliers effectively use their power perceptions to negotiate with


June 4, 2009 43 of 52

customers, they can simultaneously enhance the customer relationship as well as its profitability

from the relationship.

Lastly, our study shows the benefits of close relationships accruing to suppliers firms. Given our

findings and their own experience as a starting point, suppliers can set realistic expectations

about the desired outcomes of the relationships with customers. As mentioned earlier, the

supplier base of leading OEMs has been reduced in recent years in favor of close relationships

with a few select suppliers. In the midst of this growing trend, supplier firms may have gravitated

towards close relationships with their customers – perhaps with too much expectations about the

economic benefits of close relationships. If this is the case, the suppliers’ unmet expectations

might have contributed to their frequent disappointment and complaints about customer

relationships featured in the press. Our findings show that suppliers can benefit from close

relationships, specifically through information sharing. However, a supplier’s dependence on a

customer that leads the supplier to a close relationship in the first place is likely to antagonize a

supplier’s profitability.

8. LIMITATIONS AND FUTURE RESEARCH

This study has several limitations. Firstly, the study is based on cross-sectional data which were

collected in late 2008 through early 2009 at the beginning state of awareness of an ongoing

recession. During the data collection period, over 50% of the firms that previously agreed to

participate in the survey retrieved their commitments. Although we did not find a non-response

bias in terms of industry membership and size, it is possible that the economic downturn

introduced a selection bias to the study. For instance, it could be that supplier firms involved in

transactional relationships with customers are more vulnerable to economic turbulence and, as a

consequence, were more likely to select themselves out of this study than firms depending on a

few customers. Furthermore, macroeconomic factor may affect suppliers’ operations strategy,

power/dependence relations, and relationships between customers and supplier, not to mention

the supplier’s profitability over time. Thus, it would be interesting to conduct another cross-

sectional study in a different point in time, or a longitudinal study, to compare the effects of

operations strategy on supplier-customer relationships and suppliers’ profitability.


June 4, 2009 44 of 52

Secondly, we relied on the respondent suppliers’ perceptions of the phenomena under study,

including assessments of the customers’ dependency on the suppliers. Although we used

rigorous methods to estimate a common method bias in our measures, and found it negligible,

future studies could benefit from collecting data from both suppliers and customers. Especially,

it is possible that a customer’s dependence on a supplier is over- or under-estimated by a supplier.

This might provide a possible alternative explanation for why a customer’s dependence in this

study does not contribute to the strengthening of the relationship with a supplier and/or to the

improvement of a supplier’s profitability.

Lastly, our study involves suppliers (to manufacturing firms) operating in industries classified as

34-38 SIC and having employee bases between 100 and 1,000. It could be that our sample might

represent more of the weak supplier-powerful customer relationships than the powerful supplier-

weak customer relationships. Thus, future studies on different dyadic relationships could offer

valuable contributions to a broader understanding of the effects of operations strategy on

supplier-customer relationships and suppliers’ profitability across dyads.


June 4, 2009 45 of 52

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Appendix A: Scale Items Construct Response Format ItemSource

Conformance Quality [QUAL]

(1=strongly disagree/ 7=strongly agree/ 0=not applicable)

Our plant emphasizes the importance of good housekeeping [HKP]a We regularly perform maintenance on our production equipment [MTN]n

Cost Efficiency [EFF]

(same as above) We have our suppliers deliver to us on a just-in-time basis [JIT]b We use a value stream mapping to identify and eliminate non-value added activities [VSM]b We use a pull (or kanban) system to control work-in-process inventory [PUL]n We use small lot sizes to achieve short lead times [SLT]n

Delivery Dependability [DEL]

(same as above) We have information on quality performance visible through signs and charts [VI]a We frequently measure our ability to make on-time deliveries to the customer [OTD]n

Flexible Responsiveness [FLEX]

(same as above) Our engineers are trained to rapidly implement minor changes in current products that result from our customer’s changing requirements [ENT]d

If needed, we have the ability to rapidly introduce new products into manufacturing for the customer [NPM]n New Product Introduction [NPI]

(same as above) Our manufacturing engineers are involved to a great extent before the introduction of new products [ENI]b For the introduction of new products, we work on cross-functional teams involving members from a variety of

areas (marketing, manufacturing, etc.) [CFT]b The components (and/or assemblies) we produce for our customers are designed for ease of manufacturing

(and/or assembly) [EZMF]b New Product Development [NPD]

(same as above) We use the following product development tools:e Design of Experiment [DOE] Quality Function Deployment [QFD] Failure Mode of Effects Analysis [FMEA]

Supplier’s Dependence [SDP]

(1=strongly disagree/ 7=strongly agree)

It would be relatively easy for us to find another customer for our products(reverse) [SDP1]f It would be difficult for us to replace the profits this customer account generates [SDP2]g If the customer firm stopped buying from us, we could easily replace their volume with sales to other customer (reverse) [SDP3]f

Customer’s Dependence [CDP]

(same as above) If the customer decided to stop purchasing from us, we could easily replace this volume with purchases from other suppliers (reverse) [CDP1]f

Dealing with a new supplier would require the customer to undertake a significant redesign and development effort [CDP2]f

The customer’s total costs of switching to a competing supplier’s products would be high [CDP3]g Appendix A: Continued


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Construct Response Format ItemSource Information Sharing [INF]

(1=completely inaccurate/ 7=completely accurate)

In this relationship, it is expected that any information that might help the other party will be provided to them [INF1]h Exchange of information in this relationship takes place frequently and informally, and not only according to a pre-specified agreement [INF2]h It is expected that the parties will provide proprietary information if it can help the other party [INF3]h It is expected that we keep each other informed about events or changes that may affect the other party [INF4]h

Joint Decisions [JD] (same as above) We make joint decisions with our customer about ways to improve in the following areas:i

Our product cost [JD1] Our long-range demand [JD2] Our order entry procedures [JD3] Our delivery schedules [JD4] Our product design [JD5]

Performance from a relationship [PERF]

(1=significantly below target/ 7=significantly

over target)

How did the actual outcomes tied to your relationship with the customer compare against your stated targets for the last year?n Gross margin [GM] Sales revenues [RV] Sales growth [SG]

Control Variables (1=highly unpredictable/

7=highly predictable) Uncertainty: How would you characterize the market environment in which your company is serving your chosen customer?j (1) Rate of product innovation; (2) Rate of innovation in manufacturing processes; (3) Market segment volume upswings and downswings; (4) The expectations and demands from the customers

Continuous Length: How long has your company had a business relationship with this customer (in years)?k (1=private/ 2=public) Holding: Which of the following describes the holding status of your company?n (1=0-50M/ 7=over 300M) Firm Size: What is the size of your company in terms of sales revenue (in dollars) in the last fiscal year?k (0-$50M; $51M-$100M; $101M-$150M; $151M-$200M; $201M-$250M; $251M-$300M; Over $300M) Market Variable (1=strongly agree/

7=strongly disagree) Please answer the extent to which you agree with the following statement about yourselfl (1) I daydream a lot; (2) I often think of what might have been

Informant Eligibility (1=very bad/ 7=very

good) How would you describe your knowledge of the following topics in this survey?k (1) Operations-related topics; (2) Customer relationship-related topics; (3) Performance-related topics

a Flynn et al. (1994); b Flynn and Flynn (2004); c Ward and Duray (2000); dPagell and Krause (2004); eIttner and Larcker (1997); fHeide (1994); gKumar et al. (1995); hHeide andn Miner(1992); iJohnston et al. (2004); jAnand and Ward (2004); kGhosh and John (2005); lMalhotra et al. (2006); nNew items


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Appendix B: Scale Items Removed during Calibration Construct Response Format ItemsSource Capabilities (1=strongly disagree/

7=strongly agree/ 0=not applicable)

We make extensive use of statistical techniques to identify and reduce variance in processes [STAT]a We try to standardize our customer’s products, whenever possible, to reduce sources of variation in processes

[STD]n We make frequent shipments to our customer [SHP]b We emphasize high utilization of equipment and labor to improve cost efficiency [UTL]c Our crews practice setups to reduce the time required [STU]b We measure manufacturing lead time for all products made for the customer [LT]n We reduce lead times by creating layouts that minimize the travel distance of materials, tooling and workers

[LAY]n We make extensive use of general equipment when we manufacture products for our customer [GEQ]c We have the ability to rapidly change priorities of jobs on the shop floor [JOB]c It is easy for us to change the routings (i.e., equipment assignments) for our customer’s products [ROUT]c We make extensive use of cross-training to achieve flexibility in our labor allocation [CTN]n Our suppliers are actively involved in our new product development process [SIN]b Supplier’s Dependence [SDP]

(1=strongly disagree/ 7=strongly agree)

We would incur high costs in replacing the customer with another customer [SDP4]g

Customer’s dependence[CDP]

(same as above) There are very few suppliers who could provide the customer with products and services comparable to what we currently offers [CDP4]g

Performance from a relationship [PERF]

(1=significantly below target/ 7=significantly

over target)

Manufacturing cost per unit [MC]n Customer Satisfaction [CS]n

a Flynn et al. (1994); b Flynn and Flynn (2004); c Ward and Duray (2000); gKumar et al. (1995); nNew items Appendix C: Equations (adopted from Malhotra et al. (2006))

(1)

(2) ⁄ ,

where - CMV-adjusted correlation; - uncorrected correlation; - an estimate of CMV

the effect of operations strategy on supplier-customer ... · and suppliers’ financial...

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