The Impact of Relational Supplier Exchange on Financial Performance: A Study of the Hospital Sector

Abstract

In the hospital context, when responsiveness is high, relational exchange with suppliers associates with better financial performance. But when a hospital’s responsiveness is low, relational exchange has no effect on performance—the hospital’s inability to adopt “things that are new” and extirpate “things that are old” eclipses the potential financial benefits of long-term, relational exchange with suppliers. Furthermore, the quality orientation of the hospital and supplier uncertainty associate with relational supplier exchange—these effects are not moderated by hospital responsiveness. The net effect is that a quality orientation in a hospital impacts financial performance only when hospital responsiveness is high.

Full text

The Impact of Relational Supplier Exchange on Financial
Performance: A Study of the Hospital Sector
Richard Germain
1
, Beth Davis-Sramek
2
, Subhash C. Lonial
2
, and P. S. Raju
2
1
EBS University and St. Petersburg State University
2
University of Louisville
In the hospital context, when responsiveness is high, relational exchange with suppliers associates with better financial performance.
But when a hospital’s responsiveness is low, relational exchange has no effect on performance—the hospital’s inability to adopt
‘‘things that are new’’ and extirpate ‘‘things that are old’’ eclipses the potential financial benefits of long-term, relational exchange with
suppliers. Furthermore, the quality orientation of the hospital and supplier uncertainty associate with relational supplier exchange—
these effects are not moderated by hospital responsiveness. The net effect is that a quality orientation in a hospital impacts financial
performance only when hospital responsiveness is high.
Keywords: financial performance; hospitals; relational exchange; suppliers
INTRODUCTION
In an increasingly global landscape, firms must contend with
more uncontrollable elements that hamper both effectiveness
and efficiency, and ultimately, firm performance. With this
dynamism comes mounting uncertainty, and firms must
strategically adapt in order to mitigate the associated
inherent risks. These factors and the related strategic
responses can be examined through the lens of contingency
theory. The theory holds that firms adapt their strategic
responses to maintain ‘‘fit’’ with contextual factors to influ-
ence performance (Donaldson 2001). It builds on the central
assumption that higher levels of performance result when
firms consider the context in which strategy is crafted and
implemented.
Surprisingly, only a small stream of research in the
logistics and supply chain discipline utilizing contingency
theory has been published (Buttermann et al. 2008). Most
recently, however, a contingency theory framework was
offered that examined driving and resisting forces that affect
a firm’s strategic decision to collaborate with other supply
chain members (Fawcett et al. 2008). Broadly speaking, the
framework demonstrates how external factors largely
uncontrollable to the firm influence strategic management
initiatives to create desired performance outcomes. These
relationships are moderated, however, by internal variables
that create ‘‘resistance’’ and hamper the effectiveness of those
strategic responses and performance.
From the perspective of logistics, in the health care indus-
try it is a matter of life or death to get the right product to
the right patient at the right time with the right price and
right information on the first attempt (Gundersen 2009). The
industry has historically considered itself as being operation-
ally different from others (Jarrett 1998), but others argue
that the industry can no longer afford to have this view
(Kumar et al. 2008). It has been noted that the manufactur-
ing industry shares many similar business processes with the
health care industry, especially in many of the supply chain
management (SCM) activities (Carpenter 2008). The
challenge, however, is that supply chain costs can be more
substantial to the hospital than to other industries (Beier
1995) due to inherent complexities: (1) there is a wide array
of products with little hope of item consolidation; (2) each
item may be considered critical; (3) there is a perceived need
to supply very high levels of service; and (4) there is a need
in many cases for special handling to combat spoilage or
obsolescence (Jarrett 1998).
Little academic thought is directed to SCM in a hospital
context, nor is the existing research theoretically grounded.
Rather, the focus of SCM research has been on manufactur-
ers and in examining relationships and partnering behaviors
with an emphasis on supplier–manufacturer and ⁄or the
manufacturer–distributor relationships (Ellram and Krause
1994; Buttermann et al. 2008). However, a significant portion
of the economy is driven by nonmanufacturing industries,
and the health care industry is the largest service industry in
the United States (Kumar et al. 2008). Using contingency
theory, we examine relational supplier exchange, a strategic
response from hospitals now facing increasing uncertainty in
their supply base, as there is anecdotal evidence that
hospitals use close supplier relationships as a way to lower
inventory levels and attack waste (Andel 2007). Another goal
of this research is to develop a theoretical model that
demonstrates the interrelationships among relational supplier
exchange, supplier uncertainty, quality orientation, and
financial performance from strategic and theoretical perspec-
tives, and to thus provide managers with insights that
improve performance.
The final goal of the research is to use contingency theory
to examine the moderating role of responsiveness. While the
Fawcett et al. (2008) framework demonstrates resisting forces
that could hamper strategic responses and the resulting per-
formance improvements, contingency theory also suggests
Corresponding author:
Richard Germain, Graduate School of Management, St. Peters-
burg State University, 3 Volkhovky pereulok, St. Petersburg,
199004 Russia; E-mail: RGermain@gsom.pu.ru
Journal of Business Logistics, 2011, 32(3): 240–253
Council of Supply Chain Management Professionals

that internal variables strengthen those relationships as well.
Applying this logic in a hospital context, the theoretical
model in Figure 1 suggests that the hypothesized direct rela-
tionships should be moderated by a hospital’s responsiveness
to product and process change. We add to the body of SCM
research by extending contingency theory to explain perfor-
mance differences between hospitals that have high or low
levels of responsiveness.
In the next section, we review the literature and formally
present the hypotheses. We then discuss the sample and
scales and present findings on a sample of 175 hospitals. We
provide managerial implications, limitations, and suggestions
for further research.
LITERATURE REVIEW AND HYPOTHESES
Contingency theory
The premise of contingency theory is that a wide variety of
factors exist in combination with one another which in turn
influence the behavior in organizations. Specifically, the the-
ory considers a firm’s performance to be a function of envi-
ronmental and strategic (or firm) variables (Hatten et al.
1978). The sequential environment–strategy–performance
relationship is affected by the way firms understand changes
in the competitive environment together with the firm’s
inherent resources, allowing management to appropriately
focus competitive efforts on building relevant competencies
that will enhance firm performance. There is also the impli-
cation that simple, unconditional associations between a
firm’s strategy and environment are insufficient in explaining
organizational performance; alternately, a firm’s performance
depends on the interrelations and alignment of these factors
(Venkatraman 1989).
Although contingency theory has been a mainstay in the
strategic management literature, its attention has been more
limited in the logistics literature. In addition to the Fawcett
et al. (2008) framework, it has been applied more recently to
a handful of other studies to examine supply chain risk
(Wagner and Bode 2008), to develop archetypes based on
information technology and organizational structure vari-
ables (Buttermann et al. 2008), and to underscore the impor-
tance of logistics salience (Zacharia and Mentzer 2004).
There is also a stream of slightly older studies that used con-
tingency theory to investigate logistics as a firm capability
(Emerson and Grimm 1999), to look at a flexibility-based
strategy (Fawcett et al. 1996), and to examine global manu-
facturing strategies (Fawcett and Closs 1993). A review of
the literature revealed that there were no studies that applied
contingency theory to the health care context.
Hospital sector background
Projections indicate that the annual increase in real hospital
spending between 2000 and 2012 will be 4.8% and that by
2012 hospital spending will equal 5.6% of U.S. gross
domestic product (Shactman et al. 2003). Estimates are that
waste and inefficiency cost the industry billions of dollars
each year (Gundersen 2009). SCM becomes germane
because 30%–40% of a hospital’s budget is accounted for
by various supply chain activities (DeJohn 2006), and poor
and inefficient supply chain processes can triple or even
quadruple the cost of supplies (Carpenter 2008). Also, this
amount could be decreased significantly if hospitals were to
adopt more modern methods to manage their supply chains
(DeJohn 2006).
The most prevalent stream of research in the health care
industry comes from the operations field. With a few excep-
tions, this literature base has predominantly addressed more
tactical issues pertaining to admissions planning, staff sched-
uling, capacity planning, and various hospital activities that
utilize sophisticated modeling algorithms (Adan and Vissers
2002; Green et al. 2007; Lapierre and Ruiz 2007). The logis-
tics and supply chain literature has been relatively silent in
addressing health care issues, although there are a few excep-
tions that do have a more strategic focus. Research has
examined the influence of packaging in purchasing decisions
(Kumar et al. 2008), identified barriers to SCM implementa-
tion (McKone-Sweet et al. 2005), examined supply relation-
ships and cooperation (Van Donk 2003), examined how JIT
can reduce procurement costs (Jarrett 1998), assessed the
importance of price, quality, delivery, and service attributes
used by purchasing managers (Lambert et al. 1997), and
studied hospital pharmacy distribution management and
inventory policies (Beier 1995).
In contrast, the trade press contains hundreds of articles
with anecdotal evidence of supply chain successes, failures,
Supplier
Uncertainty
Relational
Supplier Exchange
Quality
Orientation
Financial
Performance
Moderation: effects are
stronger for hospitals with high
levels of responsiveness
H1 (+)
H2 (+)
H3 (+)
Responsiveness
H5
H4H6
Figure 1: Theoretical framework.
Impact of Relational Supplier Exchange on Financial Performance 241

and prescriptive norms for better performance, indicating a
gap between its importance in academic research and in
the business press. Additionally, there has been a call to
extend this research by integrating strategic planning to fit
into the larger framework of supply chain strategy (Butler
et al. 1996), but this context is still largely underexplored
in the literature. This research intends to bridge the hospi-
tal context with contingency theory in order to build a the-
oretical model (Figure 1) to investigate the relevant
environmental and strategic variables that influence hospital
performance.
Supplier uncertainty and relational supplier exchange
Environmental uncertainty refers to an inability to predict
the future state of factors not under the control of the firm
(Beckman et al. 2004). Generally, uncertainty exists concern-
ing demand and customer preference, competitor activities,
suppliers, and the political environment, to name a few
(Celly and Frazier 1996). Uncertainty has been central to
contingency theory (Venkatraman 1989), as it is a critical
environmental variable external to the firm, especially when
it is significant to the degree that it affects a firm’s strategic
response associated with performance (Emerson and Grimm
1999). This research focuses on the supply chain side of
uncertainty by modeling the uncertainty inherent in the hos-
pital’s supply base.
Relational supplier exchange refers to the extent to which
the exchange mode has shifted from pure market exchange
to a long-term mode as an alternative to vertical integration
(Klein et al. 1978; Cannon and Perreault 1999). The impor-
tance of developing relational exchange in buyer–seller
relationships has received significant attention in the
business-to-business context (Dwyer et al. 1987). In the
health care industry, relational supplier exchange ‘‘opens an
important avenue for containing skyrocketing costs in a time
of diminished reimbursement and rapid industry consolida-
tion’’ (Wyatt 2004, p. 72). Relational exchange may be char-
acterized by trust, the involvement of suppliers in developing
new product-service offerings and in the identification of new
markets, stricter guidelines on the quality of logistical service
and of products, and shared rewards for supply chain
improvements.
Relational exchange is an ‘‘intermediate’’ mode of
exchange (from pure market exchange on one end to vertical
integration on the other end), and uncertainty leads the firm
to a strategic response to create more relational supplier
exchanges (Williamson 1985). In a hospital context, the
purchasing process is unique because errors can create life-
threatening risks. Further, hospitals face more regulatory
obstacles than most other industries, each hospital depart-
ment has unique needs for many high-priced items with
specific delivery, storage and tracking requirements, and
uncertainty is further reflected by the rapidly advancing tech-
nology resulting in shorter equipment life cycles, thus leading
to hundreds of possible suppliers (Wyatt 2004). Because of
these factors, purchasing managers may not be able to
foresee and predict all possible states of suppliers and supply
markets. Therefore, following contingency theory logic,
supplier uncertainty should lead to a shift to relational, long-
term exchanges with suppliers.
H
1
:Supplier uncertainty and relational supplier exchange
associate positively.
Quality orientation and relational supplier exchange
Quality management has long been identified as a business
strategy subject to organizational contingencies that subse-
quently affect performance (Dean and Bowen 1994). For
example, total quality management is in measure derived
from the routinization level of a service firm’s production
technology (Silverstro 2001). Quality management as a phi-
losophy or orientation is also inherently linked to supply
management through the quality of suppliers and their
supplies (Monczka et al. 2005). Given that quality impacts
the attractiveness of a firm’s product ⁄service (Chambers
et al. 2006), and that quality is connected to SCM, we
model the quality orientation of the hospital; that is, the
extent to which the institution’s policies and resources as a
strategy are directed toward improving the quality of
products and processes. Top management leadership, the
existence and role of a quality department, and supplier
quality management are levers managers use to improve
quality (Juran 1981; Deming 1982; Leonard and Earl Sas-
ser 1982).
Supplier relationships have become more collaborative in
order for firms to compete in a relentless environment that
demands continuous quality improvements (Trent 2005).
We expect that hospitals focusing on a quality orientation
will also engage in higher levels of relational exchange with
suppliers. Effective leadership is critical to effecting organi-
zational change that improves interactions with supply
chain members (Cooper and Ellram 1993). To promote
mutually beneficial relations with suppliers, management
prioritizes quality and delivery performance over price
when selecting suppliers and certifying suppliers for mate-
rial quality (Flynn et al. 1995; Trent and Monczka 1999).
Managing supplier relationships strategically is essential to
the success of hospital–supplier relationships because these
partnerships require both a high level of commitment and
an exchange of proprietary and competitive information
(Ellram 1991). A quality orientation should lead to creating
long-term, cooperative relationships with as few suppliers
as possible to obtain quality material and ⁄or services.
Thus, supplier relationships should improve productivity of
buyers by encouraging enhanced supplier commitment to
product design and quality (Ansari and Batoul 1990).
Additionally, quality focus will lead to relationships with a
smaller number of suppliers on a long-term basis to pro-
vide solutions for quality and delivery problems because
purchasers can pay close attention to each supplier (Burt
1989; Cooper and Ellram 1993). Suppliers can also help
purchasers procure the material and equipment that can be
used most efficiently.
H
2
:Quality orientation and relational supplier exchange
associate positively.
242 R. Germain et al.

Relational supplier exchange and financial performance
A key question in the research is whether the decision to
adopt relational exchange has an impact on financial perfor-
mance within hospitals. Given that it suggests more exacting
quality standards for inputs and thus fewer errors, a smaller
number of suppliers, more open information flows, higher
levels of trust, lower levels of conflict, better timing of the
delivery of product, and longer term exchange that super-
sedes price solely as the driver of exchange mode, we expect
relational supplier exchange to associate with financial per-
formance in hospitals. Prior research has demonstrated a
link between relational supplier exchange and performance
(Flynn et al. 1995; Ho et al. 2001). For example, in the con-
sumer goods sector, collaborative planning, forecasting, and
replenishment, which represent the open flow of promotional
plans and forecast information between manufacturers and
retailers, associates with improved revenue, less inventory,
and higher retail level fill rates (Chopra and Meindl 2007).
Relational supplier exchange increases the likelihood of the
timely delivery of quality materials, which facilitates inven-
tory reductions and elimination of nonvalue-added activities
(Flynn et al. 1995). Just-in-time and relational supplier
exchange associate with improved financial performance
through reductions in supply chain inventory levels (Easton
and Jarrell 1998), which enables the firm to reduce waste,
eliminate safety stock, and create lean operations (Krajewski
and Ritzman 2001), and in improved market performance
through higher quality and service levels (Christensen et al.
2005). Hospital managers have acknowledged a link between
relational supplier exchange and performance. For example,
a 2001 study showed that 48% of a sample of California
hospitals had adopted electronic data interchange, 26% had
adopted just-in-time programs, and hospital managers felt
that inventory had been reduced as a result (Aptel and
Hamid 2001).
H
3
:Relational supplier exchange and financial performance
associate positively.
The moderating role of responsiveness
While contingency theory was utilized to develop the media-
tion chain in Figure 1, contingency theory also explores the
notion of ‘‘fit’’ through moderation. Venkatraman (1989)
proposes that contingency theory as moderation applies
when relationships between strategic variables and either its
predictor or performance variable affect relationship strength
or direction. The recent contingency theory framework
(Fawcett et al. 2008) proposes that environmental variables
that drive strategic responses can be mitigated by internal
resisting forces that impede the effective implementation of
those responses. For instance, the framework proposes that
a firm’s inflexibility to systems and processes can be a resist-
ing force (Fawcett et al. 2008, 95). Therefore, as seen in
Figure 1, the research examines the moderating role of
responsiveness in a hospital’s operations. Because contin-
gency theory has not been widely utilized in the supply chain
literature and the hospital context has been underexplored,
we build the following hypotheses using the moderation logic
offered by contingency theory.
Severe customer and competitor pressures are a reality in
today’s supply chains, forcing firms to be more responsive
(Trent 2005). Although the term ‘‘responsiveness’’ has been
widely discussed in the literature, there is no consensus defini-
tion. In general, responsiveness refers to a firm’s ability to
react quickly to changes. As with the majority of SCM
literature, it has been examined within the context of a manu-
facturing firm. With respect to services, it is considered the
willingness ⁄ability to help customer and to provide prompt
service (Crosby and LeMay 1998). Responsiveness has also
been associated with a firm’s ability to: (1) adjust quickly to
changing environmental conditions (McGinnis et al. 1995); (2)
react more quickly than the competition (McGinnis and Kohn
1993); (3) modify operations to meet supply and demand fluc-
tuations (Stank et al. 1996); and (4) respond to the needs and
wants of the firm’s target market (Morash et al. 1996). The
thread running through this stream of research is that respon-
siveness is an especially important SCM capability for firm
success and competitive advantage (Stank and Crum 1997).
In a hospital context, responsiveness is a critical factor in
providing patient care. In a manufacturing environment, not
adapting quickly could result in a stock out or a lost
customer, but in a hospital environment, a lack of responsive-
ness may result in an immeasurable cost of lost life. There-
fore, while responsiveness to patients is part of a hospital
staff’s daily routine, there is an operational side to respon-
siveness in a hospital context as well. Changes in medical
technology, treatments, government regulations, competitors,
and care practices require hospitals to continually invest and
update both products and processes (Ye et al. 2007). Further,
many factors beyond the hospital’s control, such as the
unprecedented volatility in energy and raw materials costs,
inadequate reimbursement, an increasingly complex regula-
tory environment, and the surge in technology advancements,
are challenging the ability of hospitals and their suppliers to
adapt to the turbulent environment (Gundersen 2009). Much
like a manufacturing firm, a hospital’s success may rest in its
ability to be responsive to these changes.
For the research, we borrow from the operations manage-
ment literature and adapt a two-dimensional view of respon-
siveness to apply in a hospital environment (Holweg 2005).
From a capabilities perspective, we focus on the hospital’s
ability to respond to the environment with (1) process and
(2) product change. It has been noted that the health care
industry in general has been one of the last frontiers to attack
process waste, and broken processes are often the problem
(Andel 2007). As the environment, supply bases, and technol-
ogy change, it is important that hospitals adapt their pro-
cesses to reflect those changes, thus this is the first aspect
of responsiveness. There is anecdotal evidence that various
supply chain practices have been applied to processes, such
as emergency room admissions, procurement, authorization,
and discharge, with the goal being to identify bottlenecks and
waste in order to continually refine and update the processes
to impact hospital performance (Barlow 2009).
A hospital’s ability to adapt to change in supply markets
(e.g., new product availability) is the second key dimension
Impact of Relational Supplier Exchange on Financial Performance 243

of responsiveness. One of the problems cited in hospitals
with products is that many times, a physician or administra-
tor will continue to unknowingly purchase products with
questionable track records, ones that no longer fit with the
information technology infrastructure, or ones that are no
longer being reimbursed (Carpenter 2008). Therefore, the
ability to make product changes in order to adapt to the
aforementioned environmental variables is also imperative as
a means to reduce waste and increase efficiency.
Hospitals that are unresponsive suffer from product and
process ossification. The internal culture of unresponsive
hospitals would preclude them from adopting the latest sup-
ply chain technologies and the latest product-service offerings
in favor of those that already work. In one sense, an unre-
sponsive firm may be stuck in a core rigidity (Prahalad and
Hamel 1990), wherein managers fail to adequately observe
how competencies need to be adjusted to match a changing
environment. In addition, according to the Fisher (1997)
model, unpredictability requires that supply chains adopt a
flexible strategy that responds to environmental changes.
While supplier uncertainty should lead to relational supplier
exchange (as we suggest in H
1
), hospitals that have low levels
of responsiveness are by nature slow to change. Therefore,
as the ‘‘fit’’ perspective of contingency theory suggests, there
is a ‘‘mismatch’’ in strategic alignment. Hospitals that more
quickly adapt their products and processes to uncertainty in
the supply base will be more aligned strategically to leverage
their relationships with suppliers and be more willing to
create relational supplier exchanges. Therefore,
H
4
:The relationship of supplier uncertainty with relational
supplier exchange is stronger for hospitals exhibiting
high levels of responsiveness than for ones exhibiting
low levels.
Senior managers set the tone for procedural and product
ossification. Managers might claim to want change, but may
not possess the will, vision, or talent to bring about effective
change. From a contingency perspective, unresponsiveness as
an internal barrier should moderate or lessen the effect of
quality orientation on relational supplier exchange. As our
conceptualization suggests, a quality orientation requires a
strategic vision and top management support. Processes and
products must be assessed, evaluated, and subsequently
changed or adjusted as necessary. In the highly responsive
hospital, processes and products identified as requiring
change are changed both with rapidity and with knowledge
that managers value such change. Such an approach requires
top management support (Miller and Le Breton-Miller
2006). Thus, if a quality orientation, which suggests and
requires change to implement, is undertaken by a hospital
with low levels of responsiveness, the effect on mode of
exchange should be limited because shifting the mode of
exchange requires change.
H
5
:The relationship of quality orientation with relational
supplier exchange is stronger for hospitals exhibiting
high levels of responsiveness than for ones exhibiting
low levels.
Finally, we expect that the relationship between relational
supplier exchange and financial performance to be moder-
ated by organizational responsiveness. From a contingency
perspective, the level of relational exchange with suppliers
should be dependent on internal impediments such as
organizational unresponsiveness. Implementing the strategic
response to create relational exchange with suppliers implies
that the hospitals must be willing to transform processes.
Involving suppliers in the product ⁄service development
processes should also result in continuous improvement and
ongoing product ⁄process adaptation. Hospitals should thus
expect more streamlined processes and better inventory man-
agement and procurement decisions, thereby leading to
enhanced performance. Low responsiveness creates a barrier,
an internal contingency, to transformations and possibly a
culture of ‘‘talk’’ versus ‘‘do.’’ Because of the unwillingness
to respond to needed changes, performance should therefore
be hampered.
H
6
:The relationship of relational supplier exchange with
financial performance is stronger for hospitals exhibit-
ing high levels of responsiveness than for ones exhibit-
ing low levels.
METHOD
Sample
Data for this study were collected using a questionnaire that
was mailed to the top executive of 740 hospitals in a five-
state Midwest region. This represents 97% of all hospitals in
the region and 12% of all hospitals in the United States.
Four surveys were mailed to the chief executive of each hos-
pital. Instructions on the cover letter requested the chief
executive to complete one survey and forward the other three
surveys to other senior executives, preferably vice presidents
in the area of quality, marketing, and operations. A total of
293 responses were received from 175 hospitals for a hospital
response rate of 24% (175 ⁄740). Table 1 presents the
distribution of respondents by job title. As seen there, the
most common respondent was vice president of administra-
Table 1: Respondent job title distribution
Title Frequency Percentage
V.P. Administration 65 22.2
CEO 60 20.5
Manager of quality 54 18.4
Manager of support services 46 15.7
Director of nursing 22 7.5
Director of marketing 15 5.1
CFO 12 4.1
COO 10 3.4
Director of community relations 9 3.1
Total 293 100.0
244 R. Germain et al.

tion (22.2%), followed by CEO (20.5%), quality manager
(18.4%), and manager of support services (15.7%). The
typical respondent had 8.41 years of experience with their
current employer, had been in their current job for
5.01 years, and was positioned 1.37 levels below that of the
hospital’s CEO. Approximately 37% of the hospitals sent
multiple responses while the rest sent a single response. No
significant differences were found between hospitals that sent
a single response and those that sent multiple responses.
Multiple responses from a particular hospital were averaged
across the respondents from that hospital for each variable
for n= 175.
The distribution of size in the sample was: less than 100
beds, 27%; 100–200 beds, 44%; more than 200 beds, 29%.
According to the American Hospital Association, the compa-
rable percentages for the United States are 45, 38, and 17,
respectively. The sample distribution is somewhat skewed
toward larger hospitals. As the hospitals were not preselected
on any particular ownership criterion or specialty, all types
of hospitals are represented in the sample.
Scaling
The Appendix provides details on how the variables in the
model were scaled. Supplier uncertainty was measured using
five-point scales with endpoints of ‘‘1 = very low’’ and
‘‘5 = very high’’ to assess uncertainty of suppliers of materi-
als and of suppliers of capital equipment (Burke 1984). Rela-
tional supplier exchange was measured on three items
adapted from Cannon and Perreault (1999): (1) involvement
of suppliers in the product ⁄service development process; (2)
the extent to which longer term relationships are offered to
suppliers; and (3) the extent to which suppliers are selected
based on quality rather than price or delivery schedules.
Five-point scales with ‘‘1 = very low’’ and ‘‘5 = very high’’
were used. The items capture relational exchange as they tap
relationship length (without regard to contractual specific-
ity), the open exchange of information (product ⁄process
development), and the use of evaluative factors beyond
price. The scale for quality orientation was taken from the
literature (Saraph et al. 1989). However, the instrument was
originally designed for a manufacturing context and appro-
priate modifications were made for use in a health care
context. A total of 15 items reflecting quality orientation
were measured on five-point scales with endpoints of
‘‘1 = very low’’ and ‘‘5 = very high.’’ The items tap a
range of corporate issues including top executive responsibil-
ity for quality performance, the comprehensiveness of a plan
for quality, resource deployment, and senior management
commitment. Four perceptual measures of financial perfor-
mance were used. There is considerable precedence for the
use of judgmental measures in the literature (Jaworski and
Kohli 1993; Han et al. 1998). The scale endpoints of
‘‘1 = much worse than competition’’ and ‘‘5 = much better
than competition’’ control for specific institutional types
within the broader hospital sector. Some of the items were
concerned with three-year performance (e.g., revenue growth
over the last three years) in an effort to control for unusual
periods caused by unforeseen or random effects (Miller
1991). Responsiveness was measured on two five-point scales
with endpoints of ‘‘1 = very low’’ and ‘‘5 = very high’’: (1)
the rate at which our hospital adds and deletes products ⁄ser-
vices; and (2) the rate at which our hospital adds and deletes
processes.
Reliability and validity
The 26 items in the study were subjected to a principal com-
ponents analysis and six dimensions emerged: (1) supplier
uncertainty; (2) relational supplier exchange; (3) a quality
orientation factor consisting of nine items which was labeled
quality policy (a= 0.88); (4) another quality orientation
factor consisting of six items which was labeled corporate
support (a= 0.88); (5) financial performance; and (6)
responsiveness. The first factor of the principal components
analysis accounted for 27% of the variance in the data. This
is well below the 50% level that would indicate a ‘‘same-
source’’ or common methods variance problem (Podsakoff
et al. 2003).
The sample was split into two equal-sized groups as close
as possible based upon the median of the responsiveness var-
iable (median = 3.00; n= 77 in the low group; n=98 in
the high group). To ensure paths are appropriately estimated
in the structural equations model (SEM), a set of two-group
confirmatory factor analyses (CFAs) was examined to assess
error and loading invariance across groups (Jo
¨reskog and
So
¨rbom 1993). LISREL was used with covariance matrices
as input. In the models, quality orientation is treated as a
second order factor; that is, quality policy was modeled as
the mean of nine items and corporate support was modeled
as the mean of six items and the mean scores were used as
observable indicators of quality orientation. This is in part
justified by the strong correlation between the two quality
variables (r= .72; p< .01), which suggests the presence of
a single underlying construct. The remaining constructs were
modeled at the first order level. In the first model, loadings
and errors were estimated freely across groups (v
2
=
109.033; df = 82; p= .025; RMSEA = 0.062; NNFI =
0.953; CFI = 0.965). The error variances were then declared
invariant across groups (v
2
= 120.811; df = 90; p= .017;
RMSEA = 0.063; NNFI = 0.950; CFI = 0.959) and then
both the error variances and loadings were declared invari-
ant across groups (v
2
= 129.685; df = 101; p= .029;
RMSEA = 0.057; NNFI = 0.956; CFI = 0.960). The dif-
ference between the completely freed model and the one with
error variances set equal across groups (Dv
2
= 11.778;
Ddf = 8) and between the latter and that with both error
variances and loadings set equal across groups (Dv
2
=
8.874; Ddf = 11) were not significantly different (p> .10).
The CFA results for the model with error variances and
loadings set equal across groups are summarized in Table 2.
Scale composite reliabilities (qs) range between 0.75 and
0.87 and the percentage of variance extracted ranges
between 0.51 and 0.76. The minimum standardized loading is
0.582, the minimum t-value is 7.475, and all loadings are sig-
nificant at p< .01. This provides evidence of reliability and
convergent validity and supports the overall measurement
scheme.
Impact of Relational Supplier Exchange on Financial Performance 245

RESULTS
The one-group representation of the empirical model used in
the SEM is presented in Figure 2. In the initial model, error
variances and loadings were declared invariant across
groups, but the structural paths were estimated freely in each
group. The overall model fit very well: v
2
= 139.003;
df = 106; p= .017; RMSEA = 0.060; NNFI = 0.953;
CFI = 0.955. Under the baseline model results heading,
Table 3 summarizes the results for the model with paths esti-
mated freely in each group. The effect of supplier uncertainty
on relational supplier exchange is significant in low
(c11 = 0.226; t= 1.782; p< .05) and in high responsive
hospitals (c11 = 0.268; t= 2.349; p< .01). Under the con-
strained model results heading in Table 3 is shown the
results of constraining c11 to equality across groups. That
the model fit did not significantly change when the constraint
was imposed (Dv
2
= 0.028; Ddf = 1; p> .10) suggests that
the two paths are equal. H
1
is supported (supplier
uncertainty associates positively with supplier relational
exchange), however, H
4
is not supported (responsiveness
does not moderate the effect). The effect of quality orienta-
tion on relational supplier exchange is also significant in
both groups (c12 = 0.356; t= 2.741; p< .01 for low
responsive hospitals; c12 = 0.552; t= 4.510; p< .01 for
high responsive hospitals). Constraining the path to equality
does not lead to a significant change in fit (Dv
2
= 1.844;
Ddf = 1; p> .10). H
2
is supported as quality orientation
associates positively with relational supplier exchange. H
5
is
not supported as the effect of c12 is not moderated by
responsiveness. Finally, the effect of relational supplier
exchange on financial performance is not significant for hos-
pitals with low levels of responsiveness (b21=)0.045;
t=)0.309; p> .10), but the effect is significant for hospi-
tals with high levels of responsiveness (b21 = 0.244;
t= 2.137; p< .05). Constraining the path to equality
across equality does lead to a significant change in fit
(Dv
2
= 2.998; Ddf = 1; p< .10). H
6
, but not H
3
is
Table 2: Confirmatory factor analysis results
Construct Scale composite reliability (q) Variance extracted Item Standardized loading t-value
Supplier uncertainty 0.87 0.76 x
1
0.872 14.160
x
2
0.878 14.351
Quality orientation 0.84 0.72 y
1
0.864 13.890
y
2
0.831 12.997
Relational supplier exchange 0.75 0.51 y
3
0.776 11.347
y
4
0.752 10.819
y
5
0.582 7.475
Financial performance 0.87 0.64 y
6
0.716 12.567
y
7
0.921 15.063
y
8
0.796 12.122
y
9
0.607 8.471
Notes: CFA fit statistics: v
2
= 129.685; df = 101; p= .029; RMSEA = 0.057; NNFI = 0.956; CFI = 0.960; all t-values are significant at
p< .01.
Financial
Performance
η2
Quality
Orientation
ξ2
η1
Relational
Supplier Exchange
Supplier
Uncertainty
ξ1
γ12
γ11
β21
y3
y6
x1
y4
y2 y1
y7
y8
y9
x2
y5
Figure 2: Empirical model (single group representation).
246 R. Germain et al.

supported. The effect of relational supplier exchange on
financial performance is positive when responsiveness is high,
but null when responsiveness is low.
When considered simultaneously, the hypotheses suggest
that supplier relational exchange completely mediates the
effects of supplier uncertainty and quality orientation on
financial performance and that the mediation is stronger for
highly responsive hospitals. Additional models were exam-
ined to assess this corollary. First, direct paths estimated
freely across groups from supplier uncertainty and quality
orientation to financial performance were modeled. The four
paths and change in model fit were not significant
(Dv
2
= 3.836; Ddf = 4; p> .10).
Second, a parsimonious model was assessed. In this model,
direct effects from supplier uncertainty and quality orienta-
tion to financial performance were excluded, the path from
relational supplier exchange to financial performance was
fixed to zero in the low responsiveness group, and the paths
from supplier uncertainty and from quality orientation to
relational supplier exchange were set equal across groups
(v
2
= 141.369; df = 109; p= .020; RMSEA = 0.059;
NNFI = 0.956; CFI = 0.956). The difference between this
model and the baseline model is not significant (Dv
2
=
2.336; Ddf = 3; p> .10). The total effects of supplier uncer-
tainty and quality orientation are null for hospitals with low
levels of responsiveness. However, for hospitals with high
levels of responsiveness, the total effects of supplier uncer-
tainty (t= 2.922; p< .01) and quality orientation (t=
5.089; p< .01) on financial performance are significant.
Third, two regression models were examined (Table 4).
Over and above replicating the SEM results, these models
predicting financial performance and relational supplier
exchange include responsiveness as main effects (both models
are significant at p< .01). The significance (or lack thereof)
of the interaction effects is consistent with the two-group
SEM approach. More importantly, responsiveness is sig-
nificant in both models (at p< .01). Thus, not only does
responsiveness moderate the effect of relational supplier
exchange on financial performance, higher levels associate with
relational supplier exchange and financial performance.
In sum, the results suggest that supplier uncertainty and
quality orientation do lead hospitals to create more rela-
tional exchanges with their suppliers. Contrary to what we
posited, however, these relationships are equal regardless of
a hospital’s responsiveness to product and process change.
The most insightful finding, however, demonstrates that
while relational exchange does impact financial performance
in highly responsive hospitals, the effect is null when
hospitals exhibit low levels of responsiveness. In other words,
Table 3: Summary of SEM analysis
Path
Baseline model results Constrained model results
Dv
2
(Ddf = 1)
Conclusion
Low responsiveness group High responsiveness group
Estimate (t-value) Estimate (t-value) Constraint
Model
v
2
c11 0.226 (1.782b) 0.268 (2.349a) c11 fixed = across
groups
139.031 0.028 H
1
supported; H
4
not
supported
c12 0.356 (2.741a) 0.552 (4.510a) c12 fixed = across
groups
140.847 1.844 H
2
supported; H
5
not
supported
b21 )0.045 ()0.309) 0.244 (2.137b) b21 fixed = across
groups
142.001 2.998c H
3
not supported; H
6
supported
Notes: For freed model: v
2
= 139.003; df = 106; p= .017; RMSEA = 0.060; NNFI = 0.953; CFI = 0.955; common metric completely
standardized estimates are reported; df = 107 for all restricted models; a, p< .01; b, p< .05; c, p< .10.
Table 4: Regression model results
Dependent variables
Dependent variable
Financial
performance
Relational
supplier
exchange
Estimate t-value Estimate t-value
Responsiveness 0.260 2.437a 0.174 2.435a
Relational supplier
exchange
0.064 0.836
Supplier uncertainty 0.224 3.235a
Quality orientation 0.340 4.790a
Responsiveness
·relational
supplier exchange
0.139 1.897c
Responsiveness
·supplier
uncertainty
0.019 0.278
Responsiveness
·quality
orientation
)0.007 )0.101
R
2
.093 .254
Model F 5.827a 11.481a
Notes: Estimates are standardized; mean centering was undertaken
prior to creating the interaction effects; a, p< .01; b, p< .05; c,
p< .10.
Impact of Relational Supplier Exchange on Financial Performance 247

low responsiveness acts as a ‘‘barrier’’ to financial
performance.
DISCUSSION AND CONCLUSION
Prior research has demonstrated that when firms operate in
an environment characterized by scarce resources, increasing
competition, and faster rates of change, they are more likely
to form relational ties with supply chain members (Lambert
et al. 1996). Therefore, firms in today’s global landscape are
more likely to initiate a strategic response toward closer sup-
ply chain relationships, thus shifting the competitive focus to
supply chains competing for market presence and power
(Corbett and Karmarkar 2001). The research focused on
relationships formed specifically with suppliers through
examining relational supplier exchange and its ‘‘fit’’ with
uncertainty, quality orientation, and organizational respon-
siveness from a contingency perspective. Effective supply
chains include effective relationships which feature a reliance
on trusted suppliers, as these relationships ‘‘matter’’ because
almost every industry is facing changes that make suppliers a
critical part of a firm’s value chain (Trent 2005).
The environment fistrategy fiperformance linkage is
captured through the lens of contingency theory, which
implies that firms must identify sequential, cause-and-effect
relationships among environmental, management (including
exchange regimes and quality initiatives), and performance
outcomes to ‘‘match internal features’’ with an uncertain and
changing environment (Luthans and Stewart 1977). Given
the uncertainty inherent in today’s dynamic atmosphere,
contingency theory provides a basic rationale to examine
how firms respond to environmental changes to maintain or
improve their competitive position (Fawcett and Closs 1993).
We add to the body of supply chain literature by using con-
tingency theory to demonstrate the importance of ‘‘fit’’
through examining the moderating effect of responsiveness
on linear linkages. If this ‘‘fit’’ is not achieved, opportunities
may be lost, costs may rise, and the maintenance of the firm
may be threatened (Child 1972). The contribution is to
examine these relationships by grounding them in contin-
gency theory and to emphasize that the moderating role of
responsiveness can also be a factor in a firm’s ability to cre-
ate a competitive advantage.
Another contribution to the supply chain literature is the
emphasis on the health care context. Although the trade
press has for some time emphasized the importance of SCM
to the health care and hospital industries, insufficient atten-
tion has been paid to the rigorous assessment of its role in
the hospital sector. This is a suitable context to apply contin-
gency theory because the hospital industry has shifted from
being relatively stable in the early 1980s to being subject to
constant change, and hospitals have had to adjust their strat-
egies to deal with these changes (Kumar et al. 2002). In the
midst of these changes, hospitals must maintain a balance
between delivering high-quality care and running an efficient
operation to achieve modest profits under highly regulated
and constrained payment structures (Fennell and Alexander
1993; O’Connor and Shewchuk 1995). While this context has
special nuances because of the immeasurable value of a
human life, we concluded this study by agreeing with other
researchers who have recognized that this industry has over-
lapping similarities with other types of businesses and may
indeed not be distinctly different in terms of many SCM
activities (Carpenter 2008; Kumar et al. 2008). The contin-
gency theory framework, however, can be used to tease out
those nuances and differences between contexts. For
instance, the effect of responsiveness may indeed be different
across other industries or across firms that hold a different
position in the supply chain.
Because of the context, we limit our managerial implica-
tions to the hospital sector. Managers should understand
that relational supplier exchange, while important, does not
universally lead to better financial performance. Some hospi-
tals are simply quicker on their feet when it comes to adopt-
ing that which is new and eliminating that which does not
work. Highly responsive hospitals are the ones who benefit
from relational supplier exchange. Hospitals with low levels
of responsiveness may engage in relational supplier exchange,
but they do not garner financial performance benefits. Man-
agers should understand that a quality orientation associates
with relational supplier exchange and that the association is
independent of the level of hospital responsiveness. Due to
the role of relational supplier exchange, a quality orientation
does not associate with financial performance when respon-
siveness is low. In effect, quality orientation may exist on
paper but without financial impact. This does not imply that
hospitals with lower levels of responsiveness cannot benefit
from a quality orientation in other ways. For example, a
quality orientation may impact the reputation of the hospital
even when responsiveness is low. In addition, supplier uncer-
tainty associates with relational supplier exchange and this
relationship is independent of the level of responsiveness.
These findings highlight the importance of responsiveness in
managing a hospital’s supply chain. Managers need to focus
first on ensuring that their hospital is flexible in terms of
how things get done and in terms of what product-services
are offered to the market. Once this has been achieved, only
then will the financial benefits of relational supplier exchange
(and of a quality orientation) be observed.
Limitations and further research
The research possesses inherent limitations. We modeled soft
or perceptual measures of financial performance from a
cross-sectional perspective within a sample of large hospitals.
The scales for uncertainty and responsiveness each utilized
two items. Additional research should seek to correct these
deficiencies. Furthermore, additional, contingency theory
‘‘fit’’ approaches may yield novel insights. The approach we
took was reliant upon linear effects across groups. A more
meaningful gestalt approach may identify nonlinear constel-
lations of attributes that are connected to the environment
and to performance. It may be that specific constellations of
quality, information technology, relational exchange, and
labor practice attributes are particularly effective or efficient
in specific contexts. This raises the point that information
technology has recently been applied to contingency theory
248 R. Germain et al.

‘‘fit’’ models and that it has increasingly become an
important element of how hospital supply chains are man-
aged (Aptel and Hamid 2001). A shortcoming is that our
model did not incorporate the role of information technol-
ogy and did not connect information technology to perfor-
mance. It may be that the effect of relational supplier
exchange on performance may be weaker for hospitals that
have failed to adopt various forms of electronic commerce to
manage suppliers and inventory. A second ‘‘fit’’ approach
worthy of investigation would be that of mediation. It may
be that responsiveness acts as a mediator between supplier
uncertainty and financial performance. Conceptually, the
focus would shift from relational supplier exchange to
responsiveness. This leads to the issue of potential sources of
responsiveness. It may be that some hospitals face very com-
petitive environments and responsiveness is a necessity for
survival. Some hospitals that are funded publicly may not
feel much pressure to be responsive or efficient from a supply
chain perspective. Research should focus on potential
sources of responsiveness and connect these sources directly
to hospital SCM. In addition, the sampled hospitals are
overly representative of large institutions. Finally, a number
of hospitals outsource procurement of some supplies to
third-party logistics suppliers. Theory needs to be developed
specific to the hospital context that models the role of third-
party logistics providers.
In summary, the contingency theory framework leads to
supply chain insight. While we can speculate that the main
effects model will be similar across contexts, the effect of
responsiveness in these relationships might look different in a
manufacturing or retailing context, and we encourage more
research to tease out these distinctions. Furthermore, the
contingency theory framework would also apply when exam-
ining different environmental, strategic, and performance
variables, as well as the moderating variables. For instance,
contingency theory could be applied in a manufacturing
context to explore the main effects relating to demand unpre-
dictability fiprocess integration fioperational efficiency,
with supply chain IT moderating those relationships. In sum,
contingency theory should be a viable platform to
empirically investigate multiple supply chain phenomena,
and we encourage its use in the future.
APPENDIX: MEASUREMENT
Scale ⁄Source Items Reliability Mean SD
Supplier uncertainty:
Burke (1984)*
Extent of uncertainty of the following
on the hospital of
x
1
. Suppliers of materials r= .79 3.21 0.96
x
2
. Suppliers of capital goods 3.16 0.98
Quality orientation:
Saraph et al. (1989)
y
1
. Quality policy
1. Extent to which top executives assume
responsibility for quality of performance
a= 0.88 3.87 0.83
2. Acceptance of responsibility for quality
by major department heads
3.60 0.69
3. Degree to which top management is
evaluated for quality performance
3.21 0.95
4. Extent to which top management
supports a long-term quality
improvement process
4.10 0.81
5. Extent to which top management has
objective for quality performance
3.32 1.07
6. Importance attached to quality by top
management in relation to cost ⁄revenue
objectives
3.49 0.90
7. Degree to which top management considers
quality improvement as a way to
increase profits
3.39 0.98
8. Degree of comprehensiveness of the
quality plan
3.33 0.87
9. Extent to which top management has
developed and communicated a vision
for quality as part of a strategic vision
of the organization
3.47 1.08
Continued.
Impact of Relational Supplier Exchange on Financial Performance 249

APPENDIX: (CONTINUED)
Scale ⁄Source Items Reliability Mean SD
y
2
. Corporate support
Extent to which senior management has
set goals in the area of quality
a= 0.88 3.78 0.94
Extent to which quality is considered as
a key strategic opportunity by senior
management
4.00 0.92
Quality is emphasized throughout the
hospital by senior management
3.79 0.88
Overall, in comparison to other similar
hospitals, extent to senior management
is committed to quality
3.93 0.82
The extent to which our hospital makes
available resources to carry out quality
control and quality improvement programs
3.60 0.93
The degree of appropriateness of our
current equipment, computer systems,
and facilities to carry out quality
improvement programs
2.99 1.00
Relational supplier
exchange: adapted
from Cannon and
Perreault (1999)*
y
3
. Extent to which suppliers are selected
based on quality rather than price
or delivery schedule
a= 0.76 2.84 0.95
y
4
. Involvement of suppliers in the product ⁄service
development process
2.55 0.93
y
5
. Extent to which longer term relationships are
offered to suppliers
3.06 0.94
Financial performance:
Miller (1991)
†
y
6
. Revenue growth over the last three years a= 0.87 3.40 0.99
y
7
. Market share gain over the last three years 3.16 0.90
y
8
. Net profits 3.23 0.99
y
9
. Return on assets 3.26 0.84
Responsiveness: Saraph et al. (1989)* Rate at which our hospital adds and deletes
products ⁄services
r= 0.63 2.89 0.80
Rate at which our hospital adds and deletes processes 2.81 0.82
Notes: *Five-point scales with endpoints of 1 = very low and 5 = very high.
†
Five-point scales with endpoints of 1 = much worse than competition and 5 = much better than competition.
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252 R. Germain et al.

SHORT BIOGRAPHIES
Richard Germain (PhD Michigan State University) is Pro-
fessor at EBS University (Germany) and Director of the
Deutsche Bahn and Russian Railways Center for Interna-
tional Logistics and Supply Chain Management at St. Peters-
burg State University (Russia). He has published more than
50 refereed journal articles appearing in such outlets as Jour-
nal of Marketing Research,Strategic Management Journal,
Journal of International Business Studies,Decision Science,
Journal of the Academy of Marketing Science,Journal of
Operations Management, and the Journal of Business Logis-
tics. He has authored or co-authored three books and has
made numerous presentations at national and international
conferences. He is on the editorial board of the Journal of
Business Logistics and the International Journal of Physical
Distribution and Logistics Management.
Beth Davis-Sramek (PhD University of Tennessee) is
Assistant Professor in the Department of Marketing at the
University of Louisville. Her research interests include the
role of logistics in supply chain management, the impact of
logistics service in developing customer loyalty, and the stra-
tegic role of logistics in creating competitive advantage. She
has published articles appearing in the Journal of the Acad-
emy of Marketing Science,Journal of Operations Manage-
ment,Journal of Business Logistics, International Journal of
Physical Distribution and Logistics Management, and the
International Journal of Logistics Management.
Subhash C. Lonial (PhD University of Louisville) is Pro-
fessor of Marketing in the College of Business at the Univer-
sity of Louisville. His writings among others have appeared
in the Journal of Academy of Marketing Science, Decision
Science, Journal of Consumer Psychology, International Jour-
nal of Operations and Production Management, and Journal
of Advertising Research. His primary teaching and research
interests are in marketing research and marketing strategy.
P. S. Raju (PhD University of Illinois) is Professor and
Chair of the Marketing Department in the College of Busi-
ness at the University of Louisville. His research interests are
in consumer behavior and health care marketing. His
research has appeared in several journals including Journal
of Consumer Research, Journal of Consumer Psychology,
Journal of Business Research, Journal of Service Research,
and Journal of Advertising, as well as in several conference
proceedings.
Impact of Relational Supplier Exchange on Financial Performance 253

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