Supply chain management (SCM) seeks to improve performance through
elimination of waste and more efficient use of internal and external
supplier capabilities and technology, creating a seamlessly coordinated
supply chain and thus elevating interfirm competition to inter-supply
chain competition (Anderson and Katz 1998). Many firms have recently
embraced the notion of strategic buyer-supplier relationships to (1)
improve efficiency and effectiveness across the value chain and (2)
seamlessly integrate their physical distribution function with supply
partners to achieve greater benefits.
Logistics management is the part of SCM that plans, implements and
controls the efficient, effective forward and reverse flow and storage
of goods, services and related information between the point of origin
and the point of consumption in order to meet customers'
requirements (CSCMP 2006). With the increasing globalization of markets
in the 1980s and 1990s, companies began to view logistics as more than
simply a source of cost savings and recognize it as a source of
enhancing product or service offerings as part of the broader supply
chain process to create competitive advantage (Novack, Langley and
Rinehart 1995; McDuffie, West, Welsh and Baker 2001). Consequently, SCM
places a premium on the adoption of a crossfunctional, externally
focused view of logistics (Manrodt, Holcomb and Thompson 1997).
Although anecdotal evidence has supported the conceptual linkage
between logistics and improved firm performance (e.g., Bowersox 1978;
Fuller, O'Connor and Rawlinson 1993), empirical research that
establishes the link between strategic buyer-supplier relationships and
logistics integration has been scarce. To help bridge this gap in
literature, this study explores the connection between strategic
buyer-supplier relationships and logistics integration, along with the
subsequent impact on a firm's agility performance.
The notion of strategic buyer-supplier relationships has gained
substantial momentum and supply chain partners work together to jointly
plan and execute strategic initiatives aimed at achieving customer
service improvements (Mohr and Spekman 1994). Accordingly, many firms
are adopting a strategic supply management approach to guide their
dyadic operations. Within this arrangement, the partners work together
to leverage both their assets and capabilities toward better integration
of the delivery activities to satisfy the ultimate needs of their
customers. In this study, we first identify a parsimonious set of
relational factors including (a) limited number of suppliers, (b)
long-term relationship orientation, and (c) interfirm communication to
form the domain of strategic buyer-supplier relationships so as to
investigate its possible impact on logistics integration.
[FIGURE 1 OMITTED]
The use of information technology as a means to enhance logistics
integration has been touted in the literature. In particular,
information technology has been promoted as an essential tool to ensure
the logistics objective of providing timely service. Researchers have
also examined the relationships between information technology,
integration and performance (e.g., Daugherty, Sabath and Rogers 1992;
Gustin, Stank and Daugherty 1994). Along similar lines, in this study,
we specifically test the effect of information technology on external
logistics integration. Another major contribution of this research is
the further evaluation of information technology as a moderator of the
relationship between strategic buyer-supplier relationships and
logistics integration. In addition, we examine the impact of logistics
integration on firms' agility performance because extant research,
though anecdotal and disjointed, has stressed that agility is an
essential ingredient of competitive advantage (Stalk and Hout 1990;
Jayaram, Vickery and Droge 1999; Stank, Daugherty and Ellinger 1999).
The remainder of the paper is structured as follows. The next
section develops a synthesis of the literature and the logic of the
substantive relationships among the model's constructs and state
formal hypotheses. The next section explains the research methodology,
including data collection, instrument development and measures,
hypothesis testing and results. The next section is about the discussion
and implications of the study findings. The concluding section
highlights some limitations of the study and offer suggestions for
CONCEPTUAL FRAMEWORK AND HYPOTHESES
Drawing on the "relational view" of inter-organizational
competitive advantage (Dyer and Singh 1998), the conceptual model
linking strategic buyer-supplier relationships, information technology,
external logistics integration and agility performance is depicted in
Figure 1. This framework is also grounded in the paradigm of strategic
management theory that emphasizes the development of collaborative
advantage (e.g., Kanter 1994; Dyer 2000), as opposed to competitive
advantage. Within the collaborative paradigm, the business world is
composed of a network of interdependent relationships developed and
fostered with the goal of deriving greater and mutual benefits (Chen and
Paulraj 2004a). In the following subsections, the paper presents (1) a
brief literature support for the theoretical constructs included in this
study, and (2) the logic of the substantive relationships among the
study variables and state hypotheses.
Strategic Buyer-Supplier Relationships and Logistics Integration
Strategic buyer-supplier relationships focus on initiatives that
enhance superior relational characteristics between the supply chain
members and create a win-win situation for both buyer and supplier firms
(Paulraj and Chen 2005). As SCM is built on a foundation of trust and
commitment (Kumar 1996), this study adopted three critical factors
(limited number of suppliers, long-term relationship orientation and
inter-firm communication) that have been documented to foster trust and
commitment (Chen, Paulraj and Lado 2004) to underpin the domain of
strategic buyer-supplier relationship. A brief literature and
theoretical foundation for the three factors is presented below.
Increasingly, companies are emphasizing working closely and
co-operatively with a limited number of suppliers that are trustworthy
rather than using the traditional, arms-length, adversarial mode of
conducting business with a large number of suppliers (Helper 1991;
Guimaraes, Cook and Natarajan 2002; Ogden 2006). Researchers have
documented that this relational contracting approach is a required
element of strategic buyer-supplier relationships (Helper 1991; Dyer and
Singh 1998; Ketchen and Giunipero 2004; Goffin, Lemke and Szwejczewski
2006). Apart from increasing trust and relational reliability, this
approach provides benefits including (1) fewer suppliers to contact in
case of orders given on short notice, (2) increased economies of scale
based on order volume and the learning curve effect, (3) dedicated
capacity and (4) better customer service and market penetration (De Toni
and Nassimbeni 1999).
More and more supplier contracts are becoming long-term and many
suppliers are providing information regarding their processes, quality
performance and even cost structure to the buying firm (Helper 1991).
Such close relationships mean that channel participants share risks and
rewards and are oriented for long-term relationship (Kaufman, Wood and
Theyel 2000; Kotabe, Martin and Domoto 2003). It is suggested that
companies would gain benefits by placing a larger volume of business
with fewer suppliers using long-term contracts (Hahn, Pinto and Brag
1983; Giunipero, Handfield and Eltantawy 2006). De Toni and Nassimbeni
(1999) found that a long-term perspective between the buyer and supplier
increases the intensity of buyer-supplier coordination, such as
provision of technological and managerial assistance and exchange of
information during product development and production stages. Moreover,
through a long-term relationship, the supplier will become part of a
well-managed chain and will have a lasting effect on the competitiveness
of the entire supply chain (Choi and Hartley 1996; Chen et al. 2004).
Effective interfirm communication can be characterized as frequent,
genuine, and involving personal interaction between buying and selling
personnel (Carr and Pearson 1999; Krause 1999; Kocabasoglu and Suresh
2006). Numerous researchers have found that when buyers and suppliers
communicate and share information relating to materials procurement and
product design issues, they are more likely to (1) improve the quality
of their products, (2) reduce customer response time, (3) reduce the
costs of protecting against opportunistic behavior and (4) improve cost
savings through greater product design and operational efficiencies
(Carr and Pearson 1999; Kotabe et al. 2003; Prahinksi and Benton 2004;
Giunipero et al. 2006).
Logistics provides industrial firms with time and space utilities
(Caputo and Mininno 1998). Logistics integration can be internal and
external (Stock, Greis and Kasarda 2000). Internal logistics integration
refers to the logistics integration across functional boundaries within
a firm. External logistics integration refers to the integration of
logistics activities across firm boundaries. It reflects (1) a
transformation of the manufacturing enterprise to encompass the entire
supply chain, not just an individual company, as the competitive unit
(Greis and Kasarda 1997), and (2) the extent to which the logistics
activities of a firm are integrated with the logistics activities of its
suppliers and customers. Higher levels of external logistics integration
are characterized by increased logistics-related communication, greater
coordination of the firm's logistics activities with those of its
suppliers and customers, and more blurred organizational distinctions
between the logistics activities of the firm and those of its suppliers
and customers (Stock et al. 2000).
Instead of adversarial relationships, firms are increasingly
placing their emphasis on the transformation of partnerships with their
suppliers (Jones, Hines and Rich 1997). The underlying reason is that
suppliers within a strategic relationship are more likely to be
motivated to guarantee delivery, quality and even cost to the buyer
firm, and are more willing to work closely to understand and incorporate
the buyer firm's requirements into their own operations (Levy
1997). Strategic buyer-supplier relationships predominantly involve
informal processes based on trust, mutual respect and information
sharing, the joint ownership of decision and collective responsibility
for outcomes (Griffin and Hauser 1996; Kahn 1996). Such collaboration
between the buyer and supplier firms are essential to ensure delivery of
high-quality services to customers, and facilitates the ability to
seamlessly integrate logistics activities across organizational
boundaries (Cavinato 2005). Researchers have empirically documented how
relationship commitment and trust foster greater cooperation, reduce
functional conflict and enhance integration as well as decision-making
under conditions of uncertainty and ambiguity (Morgan and Hunt 1994).
Moreover, as strategic buyer-supplier relationships, characterized by
limited number of suppliers, long-term relationship orientation and
interfirm communication, can facilitate complementary interactions among
dyadic partners that can ultimately improve logistics coordination, we
hypothesize that higher levels of strategic relationships can facilitate
increased integration of logistics activities across the supply chain
H1: Strategic buyer-supplier relationships will have a positive
effect on external logistics integration.
Information Technology and Logistics Integration
More than ever before, information technology is permeating the
supply chain at every point, transforming the way exchange-related
activities are performed and the nature of the linkages between them
(Palmer and Griffith 1998). Interorganizational systems are information
and communication technology-based systems that transcend legal
enterprise boundaries (Konsynski 1993). The goal of these systems is to
replace inventory with higher-quality or near-perfect information.
Research has shown information technology to be an effective means of
promoting collaboration between collections of firms, such as groups of
suppliers and customers organized into networks (Giunipero et al. 2006).
The strength of interorganizational systems has been particularly
important with respect to enabling the process transformation needed to
create effective networks (Greis and Kasarda 1997; Christiaanse and
Kumar 2000). These interorganizational information systems may include
direct computer-to-computer links with suppliers or simple electronic
data interchange (EDI) systems for exchanging data such as purchase
orders, invoices and advice of delivery notices or may involve more
complex transactions such as integrated cash management systems, shared
technical databases, Internet, intranet and extranet (Min and Galle
Information technology is essential in supporting strategic as well
as operational logistics decisions. Seamless material flows are achieved
by replacing the notion of a sequential and linear chain of information
exchange with a set of simultaneous information exchanges that span the
members of the supply chain (Greis and Kasarda 1997; Monczka, Trent and
Handfield 2004). Information technology enhances supply chain logistics
efficiency by providing real-time information regarding product
availability, inventory level, shipment status and production
requirements (Radstaak and Ketelaar 1998). It helps in sharing
information about markets, materials requirements forecasts, production
and delivery schedules (Webster 1995). In particular, information
technology (1) has vast potential to facilitate collaborative planning
among supply chain partners by sharing information on demand forecasts
and production schedules that dictate supply chain activities (Karoway
1997), (2) can effectively link customer demand information to upstream
supply chain functions (e.g., supply management and manufacturing) and
subsequently facilitate "pull" (demand driven) supply chain
operations (Min and Galle 1999) and (3) help eliminate nonvalue adding
activities by avoiding congestion in different supply chain partner
firms (Lee 2004). Furthermore, as information technology may contribute
to better integration by fostering communication-based competencies such
as (1) dissemination and sharing of information, and (2) communication
through proximity, frequent exchanges and collaborative
interde-pendencies (Grover and Malhotra 1997; Carr and Smeltzer 2002;
Vickery, Jayaram, Droge and Calantone 2003; Lee 2004; Sanders 2005), it
is hypothesized that information technology can lead to better
integration of the logistics activities and also moderate the
relationship between strategic buyer-supplier relationships and
H2: Information technology has a positive effect on external
H3: Information technology moderates the relationship between
strategic buyer-supplier relationships and external logistics
Logistics Integration and Agility Performance
Agility, in this study, refers to supply chain partners'
superior performance in flexibility, time, delivery and responsiveness,
four critical facets that have been frequently discussed in logistics
management literature. Flexibility, especially flexibility within
logistics process, is one of the most important antecedents of supply
chain agility (Swafford, Ghosh and Murthy 2006). Past researchers have
also recognized the strategic importance of time-based performance
(Droge, Jayaraman and Vickery 2004; Nahm, Vonderembse, Rao and
Ragu-Nathan 2006). Subsequently, they have considered various aspects of
time-based performance relative to different stages of the overall value
delivery cycle and have proposed several measures to evaluate them
(e.g., Jayaram et al. 1999; Droge et al. 2004). The frequent appearance
of the measures including delivery speed (Vickery, Droge, Yeomans and
Markland 1995) and delivery reliability/dependability (Handfield 1995)
suggests the important effect of delivery performance on agility. In
addition, the advent of time-based competition has elevated the
strategic importance of customer responsiveness (Stalk and Hout 1990).
Customer responsiveness describes a firm's ability to respond in a
timely manner to customers' needs and wants. Thus, a firm's
ability to respond promptly to customers' needs can be a source of
enduring competitive advantage (Cusumano and Yoffie 1998). Among the
benefits associated with superior customer responsiveness are (1)
greater customer loyalty and likelihood of repeat purchase; (2)
customers' increased willingness to pay premium prices for
high-quality products and services; and (3) increased ability to
continually improve the firm's product-delivery system and
effectively adapt to strategic requirements (Stalk and Hout 1990). A
recent study concludes that among the measures of time-based
performance, customer responsiveness is rated as the highest in terms of
strategic importance (Jayaram et al. 1999). As the logistics elements of
flexibility, delivery speed, delivery reliability/dependability and
customer responsiveness clearly represent key components of customer
service for most companies (Fawcett, Stanley and Smith 1997; Swafford et
al. 2006), these indicators are collectively included to measure agility
An organization's performance is only as good as the weakest
link in its supply chain. Accordingly, successful companies recognize
that the creation of superior customer value is a function of the
firm's logistics capability as well (Fawcett et al. 1997).
Logistics, a pivotal coordination mechanism, help firms manage
geographically dispersed global operations and facilitate agile
just-in-time and other time-based competitive strategies (McGrath and
Hoole 1992; Stock et al. 2000). This notion clearly reflects the
importance of logistics as (1) a coordinating mechanism among multiple
units of the enterprise, and (2) a source of customer value and
competitive advantage (Vonderembse, Tracey, Tan and Bardi 1995; Stock et
al. 2000). Therefore, in accordance with previous research that has
shown a positive linkage between logistics integration and increased
efficiency and productivity (Larson 1994; Gustin, Daugherty and Stank
1995; Frohlich and Westbrook 2001; Rosenzweig, Roth and Dean 2003;
Sanders 2005), it is conjectured that logistics integration will have a
significant impact on supplier and buyer agility.
H4: External logistics integration is positively related to agility
of the supplier and buyer firms.
A cross-sectional mail survey in the United States was utilized for
data collection. The target sample frame consisted of members of the
Institute for Supply Management[TM] (ISM) drawn from firms covered under
the two-digit SIC codes between 34 and 39 (34-Fabricated Metal
Industries, 35-Industrial Machinery and Equipment, 36-Electronic and
Other Electric Equipment, 37-Transportation Equipment, 38-Instruments
and Related Products, 39-Miscellaneous Manufacturing Industries). These
firms were selected as they are documented in the literature to be more
advanced in the implementation of various supply chain initiatives.
The title of the specific respondent being sought was typically
Vice President of Purchasing, Materials Management, and SCM or
Director/Manager of Purchasing, Material Management. A seven-point
Likert scale with end points of "strongly disagree" and
"strongly agree" was used to measure the items. The
performance indicators were measured using seven-point Likert scale with
end points of "decreased significantly" and "increased
In an effort to increase the response rate, a modified version of
Dillman's total design method was followed (Dillman 1978). All
mailings, including a cover letter, the survey, and a postage-paid
return envelope, were sent via first-class mail. Two weeks after the
initial mailing, reminder postcards were sent to all potential
respondents. To those who did not respond, a second mailing of surveys,
cover letters, and postage-paid return envelopes were mailed
approximately 28 days after the initial mailing. Of the 1,000 surveys
mailed, 46 were returned due to address discrepancies. From the
resulting sample size of 954, 232 responses were received, resulting in
a response rate of 24.3 percent. A total of 11 were discarded due to
incomplete information, resulting in an effective response rate of 23.2
percent (221/954). The final sample included 35 presidents/vice
presidents (16 percent), 138 directors (62 percent), 33 supply managers
(15 percent) and 15 others (7 percent). The respondents worked primarily
for medium to large firms with nearly 36 percent working for firms
employing more than 1,000 employees. Nearly 60 percent of the firms had
a gross income of greater than $100 million. In general, with respect to
the annual sales volume, the respondents were evenly distributed among
the different groups. The respondents were also distributed evenly among
the six SIC codes selected.
Nonresponse bias was tested in two stages. First, the sample and
the population means of demographic variables, namely, number of
employees and sales volume were compared to check for any significant
difference. The t-tests performed yielded no statistically significant
differences (at 99 percent confidence interval) between the sample and
population. Additionally, the responses of early and late waves of
returned surveys were compared to provide additional support of
nonresponse bias (Armstrong and Overton 1977). Along with the 10
demographic variables, 30 randomly selected variables measuring various
SCM constructs, including the ones contained in this study and others,
were also included in this analysis. The final sample was spilt into
two, based on the dates they were received. The early wave group
consisted of 123 responses while the late wave group consisted of 98
responses. The t-tests performed on the responses of these two groups
yielded no statistically significant differences (at 99 percent
confidence interval). These results suggest that nonresponse may not be
As this study collected data from a single respondent in each
responding firm, a test for potential common method bias was conducted.
Methodologically, this potential problem can be tested by the
Harman's single factor test (Harman 1967). According to this test,
if common method bias exists, (1) a single factor will emerge from a
factor analysis of all survey items (Podsakoff and Organ 1986), or (2)
one general factor accounting for most of the common variance existing
in the data will emerge (Doty and Glick 1998). An unrotated factor
analysis using the eigenvalue-greater-than-one criterion revealed six
distinct factors that accounted for 65 percent of the variance. The
first factor captured only 26 percent of the variance in the data. As a
single factor did not emerge and the first factor did not account for
most of the variance, common method bias does not appear to be a problem
(Frohlich and Westbrook 2001).
Measures and Instrument Development
The indicators used to measure the theoretical constructs are based
on an extensive review of related literature. Items tapping the
construct "Limited Number of Suppliers" measure the extent to
which firms increasingly emphasize close, relational contracting with a
smaller number of dedicated suppliers (Kekre, Murthi and Srinivasan
1995; Bozarth, Handfield and Das 1998; Shin, Collier and Wilson 2000).
The construct "Long-Term Relationships Orientation" is
operationalized by indicators reflecting the extent to which the buying
firm (a) expects its relationships with key suppliers to last a long
time, (b) works closely with key suppliers to improve product quality,
and (c) views the suppliers as an extension of the company; in turn, (d)
suppliers see their relationship with the buying firm as a long-term
alliance (Krause and Ellram 1997; Shin et al. 2000). The construct
"Inter-firm Communication" is operationalized to include the
extent to which the firm and its key suppliers (a) share critical,
sensitive information related to operational and strategic issues, (b)
exchange such information frequently, informally and/or in a timely
manner, (c) maintain frequent face-to-face meetings and (d) closely
monitor and stay abreast of events or changes that may affect both
parties (Krause and Ellram 1997; Carr and Pearson 1999; Carr and
The indicators of "Information Technology" are
operationized to denote the presence of direct computer-to-computer
links, electronic transactions and inter-organizational coordination
achieved using electronic links, as well as the use of advanced
information systems to track or expedite shipments (Radstaak and
Ketelaar 1998; Carr and Pearson 1999). Finally, the construct of
"Agility Performance" is measured by indicators tapping the
firm's ability to respond in a timely manner to the needs and wants
of its customers, through (a) flexibility, (b) delivery reliability, (c)
prompt response, (d) rapid confirmation of orders and (e) rapid handling
of customer complaints (Stalk and Hout 1990; Jayaram et al. 1999;
Swafford et al. 2006).
Before data collection, the content validity of the instrument was
established by grounding it in existing literature. Pretesting the
measurement instrument before the collection of data further validated
it. Researchers as well as supply management executives affiliated with
ISM were involved in this process. These experts were asked to review
the questionnaire for structure, readability, ambiguity and completeness
(Dillman 1978). The final survey instrument incorporated minor changes
to remove a few ambiguities that were discovered during this validation
process. As indicated earlier, multi-item scales were developed to
measure the theoretical constructs. The scales were tested for normality
and outliers using the Kaiser-Meyer-Olkin (KMO) measure of sampling
adequacy and the Bartlett test of sphericity. To assess the reliability
of the study constructs, the average correlation among items in a scale
is used (Cronbach 1951; Nunnally 1978). As can be seen in Appendices 1
and 2, except for limited number of suppliers, the Cronbach values
([alpha]) for all other constructs were above the cutoff value of 0.70
(Cronbach 1951; Nunnally 1978).
A second-order confirmatory factor analysis (CFA) was utilized to
establish the internal consistency of the first-order factors measuring
"strategic buyer-supplier relationships." The KMO score of
0.88 and the Bartlett test of sphericity of 1,259.76 (p<0.0001)
suggest that the data exhibits normality. Both the principal component
procedure from SPSS and the measurement models in LISREL were used. As
anticipated, most of the indicators loaded onto their underlying
first-order constructs during factor analysis using the principal
components method. The eigen values for these factors were all above the
1.0 cutoff point, while the percentage of variation was around 70
percent. The factor loadings were also above the cutoff point of 0.30
(Hair, Anderson, Tatham and Black 1998). During second-order CFA, the
items remaining from the principal component stage were used as
indicators of the first-order factors, which in turn were used as
indicators of the second-order construct "strategic buyer-supplier
relationships." The values for the model fit indices including
goodness of fit (0.94), adjusted goodness of fit (0.90), normed fit
index (0.94), non-normed fit index (0.96), root mean square residual
(0.07), root mean square error of approximation (0.04) and normed [chi
square] (1.97) illustrate that the model fits the data well. The
[R.sup.2] values for the indicators were above the cutoff value of 0.30
(Chen et al. 2004). The standardized coefficients and t-values for the
individual paths (Appendix 1) further show that all the indicators are
significantly related to their underlying theoretical constructs and
thus the second-order representation of strategic buyer-supplier
relationships is appropriate.
Construct validity and unidimensionality for the constructs of
strategic buyer-supplier relationships, external logistics integration
and information technology were established using a second measurement
model. The first-order factors (limited number of suppliers, long-term
relationship orientation and inter-firm communication) were used as
indicators of the second-order construct strategic buyer-supplier
relationships. The KMO score of 0.86 and the Bartlett test of sphericity
of 1741.75 (p<0.0001) suggest that the data exhibits normality.
Principal component procedure from SPSS and the measurement models in
LISREL were used to test this model as well. The results of these
analyses are provided in Appendix 2. As anticipated, most of the
indicators loaded onto their underlying constructs during factor
analysis using the principal components method. The eigen values for
these factors were above the 1.0 cutoff point, while the percentage of
variation was around 65 percent. The factor loadings were above the
cutoff point of 0.30 (Hair et al. 1998). The values for the CFA model
fit indices including goodness of fit (0.92), adjusted goodness of fit
(0.90), normed fit index (0.92), non-normed fit index (0.96), root mean
square residual (0.05), root mean square error of approximation (0.05)
and normed [chi square] (1.62) illustrate that the model fits the data
well and hence establish unidimensionality. The [R.sup.2] values for the
indicators were above the cutoff value of 0.30 (Chen et al. 2004). The
standardized coefficients and t-values for the individual paths as shown
in Appendix 2 further suggest that all the indicators are significantly
related to their underlying theoretical constructs and thus establish
construct validity. During these analyses, indicators that did not have
good psychometric properties were deleted from further consideration.
These analyses of validity, reliability, and unidimensionality indicate
that the theoretical definitions of strategic buyer-supplier
relationships, information technology and external logistics integration
all have good psychometric properties.
The hypothesized structural equation model (Figure 2), linking
strategic buyer-supplier relationships, information technology, external
logistics integration and agility performance measures was tested using
LISREL with variance-covariance matrices for the latent variables and
residuals used as input. The score for the latent variables was the
summated average of the items within. These scores were used as single
indicators for the corresponding latent variables. Various different
structural equation modeling methodologies, based on the pioneering
works of Kenny and Judd (1984), have been proposed to test the
interaction (product-term) effects. This study adopts the methodology
proposed by Jaccard and Wan (1996) to test the moderating effect of
information technology. As this methodology involves an extremely
complicated setup, a complete description of the procedure is omitted.
Interested readers are referred to additional sources (e.g., Jaccard and
Wan 1996; Schumacker and Marcoulides 1998) for technical information on
the various approaches available to test interaction effects.
Appendix 3 presents the indicators and associated reliability
values for performance constructs. The model parameters were estimated
using the method of maximum likelihood (Joreskog and Sorbom 1999). Most
of the model fit indices (given in Figure 3) satisfied the recommended
cutoff values, illustrating that the model fits the data very well. The
hypothesized relationships were tested using their associated
t-statistics. T-values >1.65 or 1.98 or 2.576 were considered to be
significant at the 0.10, 0.05, and 0.01 levels, respectively (Hair et
al. 1998). All hypothesized relationships were found to be significant,
of which three were significant at the 0.01 level and one was
significant at the 0.10 level.
[FIGURE 2 OMITTED]
[FIGURE 3 OMITTED]
Three of the hypotheses ([H.sub.1] ~ [H.sub.3]) linking strategic
buyer-supplier relationships and information technology to external
logistics integration were statistically significant and in the expected
directions. Specifically, the paths linking (1) strategic buyer-supplier
relationships and external logistics integration (b=0.39; p < 0.01),
(2) information technology and external logistics integration (b=0.27; p
< 0.01) and (2) the interaction (product) term and external logistics
integration (b=0.10; p < 0.05) were statistically significant. The
last hypothesis (H4) postulates a positive link between external
logistics integration and agility performance. The parameter estimate
for this path was also significant and in the expected direction
(b=0.18; p < 0.01).
DISCUSSION AND IMPLICATIONS
This study contributes to the research stream on logistics
integration by specifically investigating the relationships between
strategic buyer-supplier relationships, information technology, external
logistics integration and agility performance. In general, the results
of this research provide empirical evidence that effective external
logistics integration is engendered by strategic buyer-supplier
relationships and information technology. This study further reveals
that information technology can indeed moderate the positive link
between strategic buyer-supplier relationships and external logistics
integration. The findings of significant relationships between the
antecedents, external logistics integration and agility performance,
thus constitute a significant contribution to, and extension of, the
literature in logistics management.
Organizational researchers have long believed that strategically
managed supplier relationships help integrate information, material and
personnel between the partner firms (e.g., Morgan and Hunt 1994; Chen
and Paulraj 2004b). Drawing on this understanding, the support for
hypothesis H1 linking strategic buyer-supplier relationships and
logistics integration reflects that strategic relational partnerships
could specifically lead to superior external logistics integration. The
results also suggest that strategic buyer-supplier relationships help
foster collaborative behavior that facilitate joint planning and
processes beyond levels reached in less intensive trading relationships.
When a smaller number of suppliers are used, the portion of demand
shared with them is significantly higher, mandating a better integration
of logistics activities. Moreover, as two firms endure the relationship
over a longer period of time, they develop interaction routines and
coordination mechanisms that help them disseminate and interpret
information and better integrate their logistics activities. The results
also support the contention that the exchange of information through
interfirm communication is an essential condition for realizing the
potential benefits of collaborative relationships. Apart from many
economic benefits, interorganizational communication, by (1) reducing
communication errors, (2) facilitating information knowledge sharing and
(3) fostering learning as well as intuition, can ultimately increase
integration between the supplier and buyer firms. Therefore, it can be
concluded that, in contrast to adversarial relationships, strategic
buyer-supplier relationships, characterized by limited number of
suppliers, long-term relationships and interfirm communication, can
engender higher levels of logistics integration between the supply chain
Various researchers have suggested that information is a valuable
logistics resource. In fact, information flow has rightly been
recognized as equally important to materials flow in a value chain. A
number of authors have suggested that by substituting information for
physical inventories, the use of information technology can set the firm
apart from its competitors. The significance of hypotheses H2 and H3
support this very notion. In addition, it further reveals that
information technology not only has a direct impact on external
logistics integration, but also moderates the relationship between
strategic buyer-supplier relationships and external logistics
integration. The significant direct relationship suggests that
information technology can serve as a powerful mechanism in coordinating
suppliers and their activities. More specifically, by providing
real-time information regarding product availability, material
requirements forecast, inventory level, shipment status, production
requirements, production and delivery schedules, information technology
can greatly enhance the ability to further narrow delivery windows or
make adjustments to the existing schedules, thereby ultimately boosting
the supply chain logistics efficiency. The significant indirect effect
clearly suggests that information technology can facilitate the
development and use of collaborative communication, thereby leading to
superior logistics integration between supply chain partners. It also
supports the theoretical perspective of resource complementarity,
suggesting that information technology can contribute to integration
through leveraging other human, social and relational competencies
engendered through strategic buyer-supplier relationships.
Faced with an ever-increasing demand for better and faster service,
firms are forced to explore all options for improving their ability to
deliver service that immensely gratifies their customers. The empirical
support for hypothesis H4 suggests that external logistics integration
could be one of those strategic options that lead to higher percentage
of agile and on-time delivery of products and service to customers. The
result also shows that the seamless integration of the logistics
activities such as distribution, transportation and/or warehousing
facilities between supply chain partners is crucial for responsiveness,
flexibility and dependability. The results further provide empirical
evidence to the value-added potential of the logistics function,
suggesting that external logistics should be managed as a vital
strategic activity and that its coordination can ultimately generate a
sustainable win-win strategic advantage through the improvement of
agility performance of both the supplier and buyer firms.
Vertical disintegration, along with the globalization of markets,
has led companies to recognize the value-added potential of the
logistics function toward the achievement of sustainable competitive
advantage. Research on the notion of external logistics integration has
not been lacking. Although various factors that affect logistics
integration have been discussed in the past, no study has attempted to
systematically identify strategic buyer-supplier relationships as a
potential driving force and empirically test its impact on external
logistics integration. Realizing that strategic buyer-supplier
relationships is a profoundly complex concept, this study identifies the
factors of limited number of suppliers, long-term relationship
orientation, and interfirm communication to form its domain, and further
test its subsequent effect on logistics integration. Moreover, the
moderating effect of information technology on the relationship between
buyer-supplier relationship and logistics integration is another
premiere contribution of this study. The results of the structural
equation model were all found to be significant and support the notion
that external logistics integration is of strategic importance as it can
have a significant influence on the agility performance of both supplier
and buyer firms. The impact of strategic buyer-supplier relationships
and information technology on external logistics integration provide
empirical support to the notion that these constructs can help firms
improve the integration of their logistics activities through superior
relational and technological initiatives.
At this point, the authors acknowledge some limitations of this
study that might provide opportunities for future research. In this
study, three factors have been identified to represent the concept of
strategic buyer-supplier relationships. This concept, however, is a
profoundly complex construct and thus future research may need to
include other factors such as trust and commitment, supplier selection,
supplier integration and supplier certification. Another limitation of
this research concerns the sample population. Having drawn from a list
of ISM members, we can only claim that the results of this research are
generalizable to firms in that population. Although this study sample
covered a wide range of firms in the ISM database in terms of industry
membership and demographic variables, future research may include a
broader population of firms, including those in service industries in
order to expand the scope of generalizability of the results.
Furthermore, while firms within SIC codes between 34 and 39 are
generally more advanced in the implementation of various supply chain
initiatives, compared with those in other industries, the results of
this study may not be generalized to all industries. Finally, this study
focused on the buyer-supplier dyad as the unit of analysis, and assumed
the buying firm's perspective. Thus, there is a need to more fully
examine the nature of the exchange relationship from the supplier's
perspective so as to establish whether or not the relationship is
reciprocal and mutually beneficial. Despite these limitations, this
study paves the way for researchers and managers to more fully
capitalize on the potential of external logistics integration in
creating collaborative advantages for both buyer and supplier firms.
Anderson, M.G. and P.B. Katz. "Strategic Sourcing,"
International Journal of Logistics Management, (9:1), 1998, pp. 1-13.
Armstrong, J.S. and T.S. Overton. "Estimating Nonresponse Bias
in Mail Surveys," Journal of Marketing Research, (14:3), 1977, pp.
Bowersox, D.J. "The Logistics of the Last Quarter of the 20th
Century," Journal of Business Logistics, (1:1), 1978, pp. 1-9.
Bozarth, C., R. Handfield and A. Das. "Stages of Global
Sourcing Strategy Evolution: An Exploratory Study," Journal of
Operations Management, (16:2-3), 1998, pp. 241-255.
Caputo, M. and V. Mininno. "Configurations for Logistics
Co-Ordination: A Survey of Italian Grocery Firms," International
Journal of Physical Distribution and Logistics Management, (28:5), 1998,
Carr, A.S. and J.N. Pearson. "Strategically Managed
Buyer-Seller Relationships and Performance Outcomes," Journal of
Operations Management, (17:5), 1999, pp. 497-519.
Carr, A.S. and L.R. Smeltzer. "The Relationship Between
Information Technology Use and Buyer-Supplier Relationships: An
Exploratory Analysis of the Buying Firm's Perspective," IEEE
Transactions on Engineering Management, (49:3), 2002, pp. 293-304.
Cavinato, J.L. "Supply Chain Logistics Initiatives: Research
Implications," International Journal of Physical Distribution and
Logistics Management, (35:3), 2005, pp. 148-151.
Chen, I.J. and A. Paulraj. "Understanding Supply Chain
Management: Critical Research and a Theoretical Framework,"
International Journal of Production Research, (42:1), 2004a, pp.
Chen, I.J. and A. Paulraj. "Towards a Theory of Supply Chain
Management: The Constructs and Measurements," Journal of Operations
Management, (22:2), 2004b, pp. 119-150.
Chen, I.J., A. Paulraj and A. Lado. "Strategic Purchasing,
Supply Management, and Firm Performance," Journal of Operations
Management, (22:5), 2004, pp. 505-523.
Choi, T.Y. and J.L. Hartley. "An Exploration of Supplier
Selection Practices Across the Supply Chain," Journal of Operations
Management, (14:4), 1996, pp. 333-343.
Christiaanse, E. and K. Kumar. "ICT-Enabled Coordination of
Dynamic Supply Webs," International Journal of Physical
Distribution and Logistics Management, (30:3), 2000, pp. 268-285.
Cronbach, L.J. "Coefficient Alpha and the Internal Structure
of Tests," Psychometrika, (16), 1951, pp. 297-334.
CSCMP (Council of Supply Chain Management Professionals),
Cusumano, M.A. and D.B. Yoffie. Competing on Internet Time: Lessons
from Netscape and its Battle with Microsoft, Free Press, New York, 1998.
Daugherty, P.J., R.E. Sabath and D.S. Rogers. "Competitive
Advantage Through Customer Responsiveness," Logistics and
Transportation Review, (28:3), 1992, pp. 257-271.
De Toni, A. and G. Nassimbeni. "Buyer-Supplier Operational
Practices, Sourcing Policies and Plant Performance: Result of an
Empirical Research," International Journal of Production Research,
(37:3), 1999, pp. 597-619.
Dillman, D.A. Mail and Telephone Surveys: The Total Design Method,
Wiley, New York, 1978.
Doty, D.H. and W.H. Glick. "Common Methods Bias: Does Common
Methods Variance Really Bias Results?," Organizational Research
Methods, (1), 1998, pp. 374-406.
Droge, C., J. Jayaraman and S.K. Vickery. "The Effects of
Internal Versus External Integration Practices on Time-Based Performance
and Overall Firm Performance," Journal of Operations Management,
(22:6), 2004, pp. 557-573.
Dyer, J.H. Collaborative Advantage: Winning Through Extended
Enterprise Supplier Networks, Oxford University Press, New York, 2000.
Dyer, J.H. and H. Singh. "The Relational View: Cooperative
Strategy and Sources of Interorganizational Competitive Advantage,"
Academy of Management Review, (24:4), 1998, pp. 660-679.
Fawcett, S.E., L.L. Stanley and S.R. Smith. "Developing a
Logistics Capability to Improve the Performance of International
Operations," Journal of Business Logistics, (18:2), 1997, pp.
Frohlich, M.T. and R. Westbrook. "Demand Chain Management in
Manufacturing and Services: Web-Based Integration, Drivers and
Performance," Journal of Operations Management, (20:6), 2002, pp.
Fuller, J.B., J. O'Connor and R. Rawlinson. "Tailored
Logistics: The Next Advantage," Harvard Business Review, (71),
1993, pp. 87-98.
Giunipero, L., R.B. Handfield and R. Eltantawy. "Supply
Management's Evolution: Key Skill Sets for the Supply Manager of
the Future," International Journal of Operations and Production
Management, (26:7), 2006, pp. 822-844.
Goffin, K., F. Lemke and M. Szwejczewski. "An Exploratory
Study of 'Close' Supplier-Manufacturer Relationships,"
Journal of Operations Management, (24:2), 2006, pp. 189-209.
Greis, N.P. and J.D. Kasarda. "Enterprise Logistics in the
Information Age," California Management Review, (39:3), 1997, pp.
Griffin, A. and J. Hauser. "Integrating R & D and
Marketing: A Review and Analysis of the Literature," Journal of
Product Innovation Management, (13:1), 1996, pp. 191-215.
Grover, V. and M. Malhotra. "Business Process Re-Engineering:
A Tutorial on the Concept, Evolution, Method, Technology and
Application," Journal of Operations Management, (15), 1997, pp.
Guimaraes, T., D. Cook and N. Natarajan. "Exploring the
Importance of Business Clockspeed as a Moderator for Determinants of
Supplier Network Performance," Decision Sciences, (33:4), 2002, pp.
Gustin, C.M., T.P. Stank and P.J. Daugherty. "Computerization:
Supporting Integration," International Journal of Physical
Distribution and Logistics Management, (24:1), 1994, pp. 11-16.
Gustin, C.M., P.J. Daugherty and T.P. Stank. "The Effects of
Information Availability on Logistics Integration," Journal of
Business Logistics, (16:1), 1995, pp. 1-21.
Hahn, C.K., P.A. Pinto and D.J. Brag. "Just-in-Time Purchasing
and the Partnership Strategy," European Journal of Purchasing and
Supply Management, 1983, pp. 2-10.
Hair, J.F., R.E. Anderson, R.L. Tatham and W.C. Black. Multivariate
Data Analysis, Prentice Hall, Englewood Cliffs, NJ, 1998.
Handfield, R.B. Re-Engineering for Time-Based Competition, Quorum
Books, Westport, CT, 1995.
Harman, H.H. Modern Factor Analysis, University of Chicago Press,
Helper, S.R. "How Much Has Really Changed Between US
Automakers and Their Suppliers," Sloan Management Review, (32:4),
1991, pp. 15-28.
Jaccard, J. and C.K. Wan. LISREL Approaches to Interaction Effects
in Multiple Regression, Sage, Thousand Oaks, CA, 1996.
Jayaram, J., S.K. Vickery and C. Droge. "An Empirical Study of
Time-Based Competition in the North American Automotive Supplier
Industry," International Journal of Operations and Production
Management, (19:10), 1999, pp. 1010-1033.
Jones, D.T., P. Hines and N. Rich. "Lean Logistics,"
International Journal of Physical Distribution and Logistics Management,
(27:3), 1997, pp. 153-173.
Joreskog, K.G. and D. Sorbom. LISREL 8: User's Reference
Guide, Scientific Software, Chicago, 1999.
Kahn, K.B. "Interdepartmental Integration: A Definition and
Implications for Product Development Performance," Journal of
Product Innovation Management, (13:2), 1996, pp. 137-151.
Kanter, R.M. "Collaborative Advantage," Harvard Business
Review, (74:4), 1994, pp. 96-108.
Karoway, C. "Superior Supply Chains Pack Plenty of Byte,"
Purchasing Technology, (8:11), 1997, pp. 32-35.
Kaufman, A., C.H. Wood and G. Theyel. "Collaboration and
Technology Linkages: A Strategic Supplier Typology," Strategic
Management Journal, (21:6), 2000, pp. 649-663.
Kenny, D. and C.M. Judd. "Estimating the Nonlinear and
Interaction Effects of Latent Variables," Psychological Bulletin,
(96), 1984, pp. 201-210.
Kekre, S., B.P.S. Murthi and K. Srinivasan. "Operating
Decisions, Supplier Availability and Quality: An Empirical Study,"
Journal of Operations Management, (12), 1995, pp. 387-396.
Ketchen, D.J. and L.C. Giunipero. "The Interaction of
Strategic Management and Supply Chain Management," Industrial
Marketing Management, (33:1), 2004, pp. 51-56.
Kocabasoglu, C. and N.C. Suresh. "Strategic Sourcing: An
Empirical Investigation of the Concept and Its Practices in U.S.
Manufacturing Firms," Journal of Supply Chain Management, (42:2),
2006, pp. 4-16.
Konsynski, B.R. "Strategic Control in the Extended
Enterprise," IBM Systems Journal, (32:1), 1993, pp. 114-120.
Kotabe, M., X. Martin and H. Domoto. "Gaining from Vertical
Partnerships: Knowledge Transfer, Relationship Duration, and Supplier
Performance Improvement in the U.S. and Japanese Automotive
Industries," Strategic Management Journal, (24:4), 2003, pp.
Krause, D.R. "The Antecedents of Buying Firms' Efforts to
Improve Suppliers," Journal of Operations Management, (17:2), 1999,
Krause, D.R. and L.M. Ellram. "Critical Elements of Supplier
Development," European Journal of Purchasing and Supply Management,
(3:1), 1997, pp. 21-31.
Kumar, N. "The Power of Trust in Manufacturer-Retailer
Relationships," Harvard Business Review, (74:6), 1996, pp. 92-106.
Larson, P.D. "An Empirical Study of Inter-Organizational
Functional Integration and Total Costs," Journal of Business
Logistics, (15:1), 1994, pp. 153-169.
Lee, H.L. "The Triple-A Supply Chain," Harvard Business
Review, (82:10), 2004, pp. 102-112.
Levy, D.L. "Lean Production in an International Supply
Chain," Sloan Management Review, (38:2), 1997, pp. 94-102.
Manrodt, K.B., M.C. Holcomb and R.H. Thompson. "What's
Missing in Supply Chain Management?," Supply Chain Management
Review, (1:3), 1997, pp. 80-86.
McDuffie, J.M., S. West, J. Welsh and B. Baker. "Logistics
Transformed: The Military Enters a New Age," Supply Chain
Management Review, (5:3), 2001, pp. 92-100.
McGrath, M. and R. Hoole. "Manufacturing's New Economies
of Scale," Harvard Business Review, (70:3), 1992, pp. 94-102.
Min, H. and W.P. Galle. "Electronic Commerce Usage in
Business-to-Business Purchasing," International Journal of
Operations and Production Management, (19:9), 1999, pp. 909-921.
Mohr, J. and R. Spekman. "Characteristics of Partnership
Success: Partnership Attributes, Communication Behavior, and Conflict
Resolution Techniques," Journal of Strategic Management, (15:2),
1994, pp. 135-152.
Monczka, R., R. Trent and R. Handfield. Purchasing and Supply Chain
Management, Southwestern College Publishing, Cincinnati, OH, 2004.
Morgan, R.M. and S.D. Hunt. "The Commitment-Trust Theory of
Relationship Marketing," Journal of Marketing, (58:3), 1994, pp.
Nahm, A.Y., M.A. Vonderembse, S.S. Rao and T.S. Ragu-Nathan.
"Time-Based Manufacturing Improves Business Performance--Results
from a Survey," International Journal of Production Economics,
(101:2), 2006, pp. 213-229.
Nunnally, J.C. Psychometric Theory, McGraw-Hill, New York, 1978.
Novack, R.A., C.J. Langley and L.M. Rinehart. Creating Logistics
Value: Themes for the Future, Council of Logistics Management, Oak
Brook, IL, 1995.
Ogden, J.A. "Supply Base Reduction: An Empirical Study of
Critical Success Factors," Journal of Supply Chain Management,
(42:4), 2006, pp. 29-39.
Palmer, J.W. and D.A. Griffith. "Information Intensity: A
Paradigm for Understanding Web Site Design," Journal of Marketing
Theory and Practice, (6:3), 1998, pp. 38-42.
Paulraj, A. and I.J. Chen. "Supply Management and Supply Chain
Quality Performance," Journal of Supply Chain Management, (41:3),
2005, pp. 4-18.
Podsakoff, P.M. and D.W. Organ. "Self-Reports in
Organizational Research: Problems and Prospects," Journal of
Management, (12:4), 1986, pp. 531-544.
Prahinksi, C. and W.C. Benton. "Supplier Evaluations:
Communication Strategies to Improve Supplier Performance," Journal
of Operations Management, (22:1), 2004, pp. 39-62.
Radstaak, B.G. and M.H. Ketelaar. Worldwide Logistics: The Future
of Supply Chain Services, Holland International Distribution Council,
Hague, the Netherlands, 1998.
Rosenzweig, E.D., A. Roth and J.W. Dean. "The Influence of an
Integration Strategy on Competitive capabilities and Business
Performance: An Exploratory Study of Consumer Products
Manufacturers," Journal of Operations Management, (21:4), 2003, pp.
Sanders, N.R. "IT Alignment in Supply Chain Relationships: A
Study of Supplier Benefits," Journal of Supply Chain Management,
(41:2), 2005, pp. 4-13.
Schumacker, R.E. and G.A. Marcoulides. Interaction and Nonlinear
Effects in Structural Equation Modeling, Lawrence Erlbaum Associates,
Shin, H., D.A. Collier and D.D. Wilson. "Supply Management
Orientation and Supplier/Buyer Performance," Journal of Operations
Management, (18:3), 2000, pp. 317-333.
Stalk, G. and T.M. Hout. Competing Against Time: How Time-Based
Competition Is Reshaping Global Markets, Free Press, New York, 1990.
Stank, T.P., P.J. Daugherty and A.E. Ellinger.
"Marketing/Logistics Integration and Firm Performance,"
International Journal of Logistics Management, (10:1), 1999, pp. 11-24.
Stock, G.N., N.P. Greis and J.D. Kasarda. "Enterprise
Logistics and Supply Chain Structure: The Role of Fit," Journal of
Operations Management, (18:5), 2000, pp. 531-547.
Swafford, P.M., S. Ghosh and N. Murthy. "The Antecedents of
Supply Chian Agility of a Firm: Scale Development and Model
Testing," Journal of Operations Management, (24:2), 2006, pp.
Vickery, S.K., C. Droge, J.M. Yeomans and R.E. Markland.
"Time-Based Competition in the Furniture Industry: An Empirical
Study," Production and Inventory Management Journal, (36:4), 1995,
Vickery, S.K., J. Jayaram, C. Droge and R. Calantone. "The
Effects of an Integrative Supply Chain Strategy on Customer Service and
Financial Performance: An Analysis of Direct Versus Indirect
Relationships," Journal of Operations Management, (21:5), 2003, pp.
Vonderembse, M., M. Tracey, C.L. Tan and E.J. Bardi. "Current
Purchasing Practices and JIT: Some of the Effects on Inbound
Logistics," International Journal of Physical Distribution and
Logistics Management, (25:3), 1995, pp. 33-48.
Webster, J. "Network of Collaboration or Conflict? Electronic
Data Interchange and Power in the Supply Chain," Journal of
Strategic Information Systems, (4:1), 1995, pp. 31-42.
Antony Paulraj is an assistant professor of operations management
in the Coggin College of Business at the University of North Florida in
Injazz J. Chen is a professor of operations management in the Nance
College of Business Administration at Cleveland State University in
Appendix 1 Strategic Buyer-Supplier Relationships: Second-Order Factor
Indicator Component Measurement Model
(Cronbach's Alpha, Eigen Factor Std.
value) Loading Coefficient [R.sup.2] t-Value
Limited number of
0.65; Eigen value=
We rely on a small 0.83 0.69 0.46 -
We maintain close 0.82 0.72 0.54 6.25
a limited pool of
We get multiple
price quotes from
We drop suppliers
for price reasons*
We use hedging
We expect our 0.85 0.67 0.63 -
key suppliers to
last a long time
We work with key 0.67 0.74 0.72 9.24
quality in the
The suppliers see 0.87 0.79 0.48 11.90
as a long-term
We view our 0.74 0.85 0.51 10.07
suppliers as an
extension of our
We give a fair
profit share to
The relationship we
have with key
We share sensitive 0.69 0.59 0.35 -
Suppliers are 0.72 0.68 0.46 8.91
provided with any
might help them
Exchange of 0.83 0.87 0.75 9.25
in a timely manner
We keep each other 0.78 0.86 0.74 9.15
events or changes
that may affect
the other party
We have frequent 0.76 0.71 0.50 8.03
Limited number of 0.68 0.47 6.52
Long-term 0.78 0.61 7.83
Inter-firm 0.89 0.80 7.67
*Items dropped after exploratory factor analysis.
**Items dropped after confirmatory factor analysis.
Model fit indices: Normed [chi square]=1.97
([less than or equal to]5.0); Goodness of fit index=0.94
([greater than or equal to]0.90); Adjusted Goodness of fit index=0.90
([greater than or equal to]0.80); Normed fit index=0.94
([greater than or equal to]0.90); Non-normed fit index=0.96
([greater than or equal to]0.90); Comparative fit index=0.97
([greater than or equal to]0.90); Root mean square residual=0.07
([less than or equal to]0.10); Root mean square error of
approximation=0.04 ([less than or equal to]0.10)
Appendix 2 Factor Analysis Result for Constructs included in the
Indicator Component Measurement Model
(Cronbach's Alpha, Eigen Factor Std.
value) Loading Coefficient [R.sup.2] t-Value
Limited number of 0.81 0.54 0.30 -
Long-term relationship 0.74 0.67 0.45 6.98
Inter-firm 0.78 0.88 0.78 6.93
There are direct 0.77 0.69 0.47 -
links with key
Inter-organizational 0.72 0.73 0.54 9.36
We use information 0.77 0.80 0.64 9.97
We have electronic 0.64 0.56 0.31 7.39
with our key
We use electronic 0.68 0.55 0.30 8.16
transfer of purchase
We use advanced 0.74 0.73 0.53 9.30
to track and/or
Inter-organizational 0.78 0.74 0.55 -
Our logistics 0.84 0.79 0.62 14.43
activities are well
integrated with the
of our suppliers
We have a seamless 0.86 0.85 0.72 12.46
with our key
Our logistics 0.84 0.84 0.71 12.38
The inbound and 0.87 0.86 0.74 12.59
goods with our
suppliers is well
Information and 0.65 0.65 0.42 9.27
smoothly between our
supplier firms and
Model fit indices: normed [chi square]=1.62
([less than or equal to]5.0); goodness of fit index=0.92
([greater than or equal to]0.90); adjusted goodness of fit index=0.90
([greater than or equal to]0.80); normed fit index=0.92
([greater than or equal to]0.90); non-normed fit index=0.96
([greater than or equal to]0.90); comparative fit index=0.97
([greater than or equal to]0.90); root mean square residual=0.05
([less than or equal to]0.10); root mean square error of
approximation=0.05 ([less than or equal to] 0.10)
Appendix 3 Indicators measuring Agility Performance
Agility performance ([alpha]=0.86)
Rapid confirmation of customer orders
Rapid handling of customer complaints