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留学作业网提供美国俄克拉荷马大学供应链管理硕士论文定制DEVELOPING AND MEASURING SUPPLY CHAIN MANAGEMENT CONCEPTS
bySoonhong Min
The University of Oklahoma
and
John T. Mentzer
The University ofTennessee
The term supply chain management (SCM) has risen to prominence in the past ten years(Cooper et al. 1997). Supply chain management extends the concept of functional integration (i.e., theintegration of traditional business functions, departments, and processes) beyond a firm to all the firmsin the supply chain (Cooper and Ellram 1993; Cooper et al. 1997: Ellram and Cooper 1990; Greene1991) and, thus, individual members of a supply chain help each other improve the competitivenessof the supply chain, which should improve competitiveness for all supply chain members (BowersoxandCloss 1996;Cavinato 1992; Cooper and Ellram 1993; Lee and Billington 1992). A strategic proactiveapproach to managing the supply chain is critical for survival (Monczka, Trent, and Handfield1998). For example, widely adopted SCM practices today (e.g.. Efficient Consumer Response,
Quick Response, and Collaborative Planning, Forecasting, and Replenishment) are indeed proactive,cooperative activities that require joint forecasting and planning, information sharing, joint inventorymanagement, and joint control to eliminate wastes throughout the supply chain and enhance customerservice for the purpose of obtaining competitive advantage for the supply chain members as weli asthe supply chain as a whole. Therefore, Christopher (1992) proposed the real competition is not companyagainst company, but rather supply chain against supply chain.
Despite the proliferation of SCM literature, there have been few, if any, studies to developmeasurement scales of SCM-related concepts. The role of scientific inquiry is to establish the relationshipsamong the constructs of the theory, some of which must be related to observable data(Churchill 1999). In other words, without operationalizing the SCM-related constructs, we cannot furtheradvance scientific knowledge of the phenomenon, nor promote the successful application of SCM
in practice. Mentzer et al. (2001), therefore, called for empirical research to test the structure of SCM.
This paper is an answer to the research call and develops measurement scales of the SCM-related constructs:
a supply chain orientation (SCO) and supply chain management (SCM). A SCO is defined as
the implementation by an organization ofthe systemic, strategic implications ofthe tactical activities
involved in managing the various flows in a supply chain' and SCM as the systemic, strategic
64 MIN AND MENTZER
coordination ofthe traditional business functions and the tactics across these business functions withina particular company and across businesses within the supply chain, for the purposes of improving thelong-term performance of the individual companies and the supply chain as a whole (Mentzeret al. 2001). Accordingly, we developed the measurement scales, tested the validity and reliability ofthe developed scales, and verified the positive relationships among a SCO, SCM. and business performance.#p#分页标题#e#
This paper consists of four major sections. First, we summarize the theoretical framework of a
SCO and SCM. Second, we discuss the process used to develop valid and reliable measurement scalesofa SCO and SCM. Third, we test and confirm the nomological validity of the developjed scales bystatistically testing the SCO-SCM-Business Performance path. Fourth, we discuss the implicationsof the findings.
A THEORY OF SUPPLY CHAIN MANAGEMENT
Authors (La Londe and Masters 1994; Lambert, Stock, and Ellram 1998; Mentzer et al. 2001)regard a supply chain as a set of firms involved in the upstream and downstream fiows of products, services,infomation, and/or finances. For example, Mentzer et al. (2(X)I, p. 4) described a supply chainas "a set of three or more organizations directly linked by one or more ofthe upstream and downstreamflows of products, services, finances, and information from a .source to a customer." Thus, the natureof a supply chain is comprehensive so that membership is not limited to a supplier, a manufacturer,and a distributor, but open to any firm that perfonns various fiow-related services (Mentzer et al. 2(X) 1).Supply chain management should be investigated in the context of managed supply chains thatare organized through the collective efforts of supply chain members and, thus, are distinguished from
supply chains (Mentzer et al. 2001). Since the early 1970s, the SCM concept has evolved to integrate
major business processes through interfunctional coordination and interfirm cooperation for better
customer service and cost savings.
Regardless ofthe popularity ofthe SCM concept, due to different conceptualizations of SCM
by different authors, a theoretical framework of SCM was needed. Examples of these different
conceptualizations of SCM include; (1) nothing more than a different name for integrated logistics
(Tyndalletal. 1998); (2) a management process (La Londe 1997); (3) a form of vertical integration
of firms (Cooper and Ellram 1993); and (4) a management philosophy (Ellram and Cooper 1990), Consequently,
Mentzer et al. (2001) proposed different terms should be used to represent different
aspects of supply chain management: a supply chain orientation (SCO) and supply chain management
(SCM).
The foundation ofthe SCO and SCM concepts is that supply chain management as an integrative
philosophy guides firms to manage the fiows from supplier to ultimate user in a synchronized way(Cooper and Ellram 1993; Cooper etal. 1997; Ellram and Cooper 1990; Greene 1991; Houlihan 1985;
Ross 1998). In addition. SCM philosophy instructs supply chain members to focus on developingsolutions to create customer value (Langley and Holcomb 1992; Ross 1998; Tyndall et al. 1998).
JOURNAL OF BUSINESS LOGISTICS, Vol. 25, No 1,2004 65
According to Mentzer et al. (2(X)I, p. 7), SCM philosophy has the following characteristics:
1. A systems approach to viewing the supply chain as a whole, and to managing the total flowof goods from the supplier to the ultimate customer,#p#分页标题#e#
2. A strategic orientation toward cooperative efforts to synchronize and converge intrafirmand interfirm operational and strategic capabilities into a unified whole, and
3. A customer focus to create unique and individualized sources of customer value, leadingto customer satisfaction.
To set the SCM philosophy in motion, managers first need specific behavioral guidelineswithin the firm's boundaries. Thus, Mentzer et al. (2001) emphasized the importance of embracingthe SCM philo.sophy within a firm and called it a supply chain orientation. Without developing a SCOinside a firm, it is not possible to implement our conceptualization of supply chain management acrossthe firms within the supply chain. Thus, it is proposed that a supply chain oriented firm shouldbuild and maintain the following cultural elements of relations with its supply chain partners: trust,commitment, cooperative norms, organizational compatibility, and top management support.^ Trust,which consists of credibility and benevolence, determines cooperation as well as relationship commitment(Achrol 1991; Morgan and Hunt 1994). Credibility is a firm's belief that its partner standsby its word (Anderson and Narus 1990), fulfills promised role obligations, and is sincere (Dwyer and
Oh 1987; Scheer and Stem 1992). Benevolence is a firm's belief that its partner is interested in thefirm's welfare (Deutsch 1958; Larzelere and Huston 1980; Rempel, Holmes, and Zanna 1985), iswilling to accept short term dislocations (Anderson, Lodish, and Weitz 1987), and will not take unexpectedactions that would have a negative impact on the firm (Anderson and Narus 1990). Dwyer,
Schurr, and Oh (1987, p. 19) defmed commitment as "an implicitorexplicit pledge of relational continuity
between exchange partners." Siguaw. Simpson, and Baker (1998, p. 102) defined cooperative
norms as "the perception of the joint efforts of both the supplier and distributor to achieve mutual and
individual goals successfully while refi-aining from opportunistic actions." Compatible corporate culture
and management techniques of each finn in a supply chain are necessary for successful SCM (e.g..
Cooper et al. 1997; Cooper, Lambert, and Pagh 1997; Lambert, Stock, and Ellram 1998). Finally, top
management support, which includes leadership and commitment to change, is an important
antecedent to SCM (Lambert, Stock, and Ellram 1998), and the absence of it is a barrier to SCM
(Loforte 1993). In summary, a supply chain oriented firm should incubate, retain, and even improve
these five elements inside the firm with respect to its supply chain relationships.
留学作业网提供美国俄克拉荷马大学供应链管理硕士论文定制Managing a supply chain requires each firm in a supply chain to be supply chain orientedinside the firm and, at the same time, perform a specific set of collective managerial actions across thefirms within the supply chain. Tlius, Mentzer et al. (2001, p. 18) defined supply chain managementas "the systemic, strategic coordination ofthe traditional business functions and the tactics acrossthese business functions within a particular company and across businesses within the supply chain,for the purposes of improving the long-term performance of the individual companies and thesupply chain as a whole." Concerning the functional boundary of SCM, although logistics has#p#分页标题#e#
66 MIN AND MENTZER
continued to and will have a significant impact on the concept of supply chain management, authors(e.g., Bechtel and Jayaram 1997; Bowersox 1997: Cavinato 1992; Mentzer 1993; Mentzer et al. 2001;Min and Mentzer 2(XX)) argued every firm function - including logistics, manufacturing, purchasing,marketing research, promotion, sales, research and development, product design, and total systems/value analysis - is included in the scope of supply chain management.
Authors (e.g.. Cooper and Ellram 1993; Cooper etal. 1997; Cooper, Lambert, and Pagh 1997;
Ellram and Cooper 1990; Novack, Langley, and Rinehart 1995) have suggested the key componentsof SCM are collective efforts to manage supply chains as a whole, including agreement on thevision and focus for serving customers, mutually sharing information, mutually sharing risks andrewards, cooperation, integration of processes, building and maintaining long-term relationships, andagreement on supply chain leadership.' There should be an agreement on the vision and focus for serving
customers for SCM (Lambert, Stock, and Ellram 1998). Mutually sharing information among thesupply chain members is required, especially for planning and control processes (e.g., Cooper et al.1997; Cooper, Lambert, and Pagh 1997: Ellram and Cooper 1990: Novack, Langley, and Rinehart1995). Effective SCM also requires mutually sharing risks and rewards that generate a competitiveadvantage (Ellram and Cooper 1990). Cooperation refers to similar or complementary coordinated
activities performed by the supply chain members to produce superior mutual or singular outcomesthat are mutually expected over time (Anderson and Narus 1990). The implementation of SCMneeds the integration of processes across time and place in a supply chain from sourcing, to manufacturing,and to distribution (Cooper, Lambert, and Pagh 1997). EfFective SCM requires partners build,
maintain, and enhance long-term relationships (Cooper et al. 1997; Ellram and Cooper 1990).
Finally, there should be agreement on supply chain leadership for coordinating and overseeing thewhole supply chain (Lambert, Stock, and Ellram 1998).
To recap, the systems approach, cross-functional coordination, and customer focus are the
three dimensions of SCM philosophy. Each of which is, in fact, implemented in the form of SCO andSCM activities as described above. First, the systems approach views the complete supply chain as
the source ofcreating customer value and thus success in the market (cf.Kotler 1997). Three factorsin SCO (i.e., cooperative norms, top management support, and compatible culture), and two in
SCM (i.e., agreement on SCM vision and goals, and risk and reward sharing) reflect this particulardimension. Second, cross-functional coordination is, as conceptualized by several authors (cf.
Cooper and Ellram 1993; Cooper etal. 1997; Ellram and Cooper 1990; Greene l991;Houlihan 1985;
Mentzer et al. 2(X) I; Ross 1998), functional integration across the firms within a managed supply chain.#p#分页标题#e#
The dimension of cross-functional coordination across supply chain partners is measured with suchfactors as information sharing, coordination, process integration, and agreement on supply chain leadershipin SCM. Third, in the SCM philosophy in a network context, the customer is part of the supplychain and what companies can oifer to their immediate customers depends on the supply chainrelationships with suppliers as well as customers of the immediate customers (cf. HSkSnsson andSnehota 1995). Therefore, in SCM, close ties with customers as well as suppliers are essential to
understand and fulfill customer requirements. In other words, customer focus does not necessarily
JOURNAL OF BUSINESS LOGISTICS. Vol. 25. No. 1,2004 67mean focusing on just the immediate customer per se but, rather, requires focusing on both upstreamand downstream relationships. Therefore, relational factors such as trust and commitment to supplychain relationships in SCO, and building, maintaining, and enhancing long-term relationships withsupply chain partners in SCM capture the customer focus dimension.
In summary, a SCO is an implementation of SCM philosophy in individual firms in a supplychain, and SCM is all the overt management actions undertaken to realize the SCM philosophy across
the firms within the supply chain. Thus, firms implementing SCM collectively in the supply chain must
firstrealizeaSCOinsidetheirfirmsand, thus, a SCO functions as an antecedent of SCM.
METHODOLOGY
The purpose of this study was to develop the measurement scales of a SCO and SCM in accordancewith the theoretical framework proposed by Mentzer et al. (2001). ln addition, a measurementscale of a firm's business performance was developed for the purpose of testing the nomological validityof the developed SCO and SCM scales. The general conceptual framework for the measurementscale development and test is illustrated in Figure 1.
FIGURE 1
GENERAL THEORETICAL FRAMEWORK
fferings
(Timeliness
68 MIN AND MENTZER
A survey research design was used to collect data for the scale development. The authorsdeveloped and administered a questionnaire, following Dillman's (1978) total design approach.
All variables of interest were estimated through respondents' perceptual evaluation on a sevenpointLikert scale: the response categories for each item were anchored by 1 (strongly disagree) and
7 (strongly agree). The authors sent out the first survey packets, each of which included a personalizedcover letter asking for participation in this study, together with a copy of the self-administeredwritten questionnaire, and a preaddressed postage-paid return envelope, to the target respondents, followedby two additional waves of packets to those who did not respond during the targeted response
period. Questionnaire items used in the final test are presented in Appendix I.
The target firms were not limited to those in any single industry, but open to firms in variousindustries because the theory of SCM should be applicable to many industries and organizations. The#p#分页标题#e#
target respondents were executives and managers who were: (1) able to identify at least one supplychain to which their firms belonged, and (2) responsible for supply chain management. The principalassumption of this study was that the target respondents understood the concepts of a supply chainorientation and supply chain management, and were well exposed to the issues ofimplementing theseconcepts in practice. Based upon exploratory in-depth interviews with senior executives or senior-levellogistics/supply chain managers, the authors were confident that the target respondents were qualifiedto provide valid responses to this study. Target respondents were identified from the Member Rosterof the Council of Logistics Management and randomly selected for the sample after sorting outnon-SCM-related jobs and/or firms (i.e., positions in such areas as law, recruiting, public accounting,management consulting, etc.), based upon job titles and company names. The industry profile of the
final test is offered in Table 1.
TABLE 1
INDUSTRY PROFILE
Industry Classificatiun Count %
Manufacluring 169 55.%%
Services 77 25.50%
Distribution 54 17.88%
Public Services 2 0.66%
Total 302 100.00%
Structural Equation Modeling (SEM) was used as the main statistical analysis tool to purify themeasurement items. SEM is a powerful technique that combines the measurement model (confimiatoryfactor analysis) and the structural model (path analysis) into a simultaneous statistical test (Aaker and
Bagozzi 1979; Garver and Mentzer 1999). SEM was used in this study because: (1) it is useful
JOURNAL OF BUSINESS LOGISTICS. Vol. 25. No. 1,2004 69
when one dependent variable becomes an independent variable in subsequent dependence relationships;
(2) it provides a straightforward method of dealing with multiple relationships simultaneouslywhile providing statistical efficiency; and (3) it is able to assess the relationships
comprehensively and provides a transition from exploratory to confirmatory analysis (Hair et al. 1998).Therefore, SEM helped to systematically develop the measurement scales ofthe SCO and SCM constructsin a nomological web (i.e., the expected relationships that exist among a set of different but
related constructs).
DATA COLLECTION
The questionnaire packets were distributed to a total of 2,680 target respondents (i.e., 1.312 forthe pretest and 1,368 for the final test) and 442 usable responses (i.e., 140 for the pretest and 302 forthe final test) were received. The effective response rates for the pretest and the final test were12.4% and 24.67%, respectively, after undeliverable questionnaires were eliminated. The possibilityof nonresponse bias in the pretest was checked by comparing early and late respondents for all ofthe constructs through ANOVA and no significant differences were found (Armstrong and Overton
1977). Examining the possibility of nonresponse bias in the final test was more rigorous in that it was#p#分页标题#e#
checked by: (1) comparing early and late respondents for all of the constructs through ANOVA
(Armstrong and Overton 1977). and (2) comparing all respondents and 30 randomly-contacted
non-respondents on five non-demographic questions in the questionnaire through ANOVA (Mentzer
and Flint 1997). Both methods showed no statistically significant differences. Thus, it was concluded
that no evidence of nonresponse bias existed.
DEVELOPMENT OF MEASUREMENT ITEMS
The constructs of SCO and SCM are second order factors that are higher in abstraction and have
numerous first order factors imbedded within the second order factor (cf Anderson, Gerbing, and
Hunter 1987; Gerbing and Anderson 1988). The business performance construct, a byproduct of this
study, was also considered a second order factor. The authors developed, adapted, and/or adopted multiitem
measures to evaluate each construct (cf. Churchill 1979). To develop measurement items, the
process recommended by several authors in the literature was followed (Bienstock, Mentzer, and Bird
1997; Churchill 1979; Dunn, Seaker. and Waller 1994; Gerbing and Anderson 1988): (1) item generation
through literature review and experience survey/interviews with industry experts; (2) academic
expert review; (3) debriefing with industry experts; and (4) item purification with managers in
the pretest and final study. Item development and refinement does not stop at any one of these
stages but, rather, is an iterative process.
First, we generated a large pool of items for the constructs of SCO. SCM, and business performance
through a literature review and an experience survey with industry experts (cf. Bienstock,
Mentzer, and Bird 1997). Twenty-eight exploratory in-depth interviews were conducted with executives
and managers from 18 different companies. Previous studies also provided items adoptable or
adaptable to measure the constructs of SCO (Bucklin and Sengupta 1993; Cannon and Perreault 1999;
70 MIN AND MENTZER
Jaworski and Kohli 1993; Kumar. Scheer, and Steenkamp 1995; Ruekert and Walker 1987; Siguaw,
Simpson, and Baker 1998), SCM (Bowersox. Closs, and Stank 1999; Naidu et al. 1999; Spekman,
Kamauff. and Myhr 1998). and business performance (Bienstock, Mentzer, and Bird 1997; Bower-
SOX, Closs. and Stank 1999; Global Logistics Research Team 1995; Matsuno, Mentzer. and Rentz
2000). Based upon 28 experience interviews and the literature review, the domain of each construct
was tapped as closely as possible. Concepts such as SCO and SCM are latent variables that cannot
be observed directly, Thus, a fundamental principle in science is that any particular construct or
trait should be measurable by at least two, and preferably more, different methods and, as a result,
researchers are much better served with multi-item than single-item measures of their constructs
(Churchill 1979). From this pool of items, a subset was selected based upon the criteria of uniqueness#p#分页标题#e#
and the ability to convey "different shades of meaning" to respondents through content and face validity
tests (cf. Churchill 1979). The unique, different dimensions of each item were put to the test again
in the process of statistical analysis (i.e., SEM).
Five academic experts reviewed the cover letter and questionnaire that contained the measurement
items developed through the literature review and the experience survey and interviews, as
suggested by Bienstock, Mentzer, and Bird (1997). Academic experts evaluated measurement items,
drafts of the questionnaire, and cover letter from the standpoint of domain representativeness, item
specificity, clarity of con.'itruction, and readability (i.e., content validity and face validity). Based upon
the reviews, some of the measurement items were eliminated or reworded, and others were added.
The interview participants then reviewed and provided comments on the developed measurement
items. In this process, called "debriefing" (cf. Bienstock, Mentzer, and Bird 1997), the participants
were asked to complete a questionnaire that included the items, verify any ambiguity or other difficulties
they experienced in responding to the items, and offer any suggestions to improve the questionnaire.
Based upon the feedback received from 35 executives and managers, some items were
rewritten or eliminated, and others were added.
Finally, the scale purification was iterative through the pretest and the final test. To support the
deletion and/or revision decision, the results of the SEM analysis, as well as qualitative assessment
basedupon the results of the content and face validity tests, were utilized.
MEASUREMENT ITEM PURIFICATION
Prior to purification of the measurement items in each test, basic statistical analyses of the collected
data were performed, such a.s examination for incorrect coding, mean, minimum and maximum
values, standard deviation, and normality tests (i.e., skewness and kurtosis). No indication of serious
violations of univariate normality was detected.
The primary approaches for measurement item purification for both the pretest and the main test
included multiple iterations of confirmatory factor analysis (CFA), with the maximum likelihood estimation
(MLE) method that iteratively improves parameter estimates to minimize a specified fit
function, and is tolerant of sample sizes as small as 50 (Hair etal. 1998). In addition to the statistical
JOURNAL OF BUSINESS LOGISTICS. Vol. 25. Na 1,2004 71
analyses, if necessary, theoretical assessment was made before final deletion of any measurement items.
First, the hypothesized model should be modified, if necessary, based upon such indicators as
oftending estimates, squared multiple correlations, standardized residual covariances, modification
indices, as well as qualitative review. Offending estimates (i.e., Heywood Cases) such as negative#p#分页标题#e#
error terms, standardized coefficients exceeding or very close to 1.0, and very lai^e standard errors
associated with any estimated coefficients were checked (Hair et al. 1998). Squared multiple correlations
(SMC) were also reviewed to locate any relatively small SMC values that indicate the portion
of a variable's variance that is accounted for by its predictor is minimal at best (Joreskog and Sorbom
1989). Any SMC values of 0.20 or less were put to the test of deletion in the pretest, whereas any SMC
valuesequal to or less than 0.10 were treated as relatively small in the final test (in consideration of
the fact that variables were retested in the fmal test). Standardized residuals are the differences
between the observed covariance and the estimated covarlance matrix, and significant residuals
(i.e., greater than 12.581. which is statistically significant at the 0.05 level) indicate a substantial
predictionerrorforapairof indicators (i.e., one of the covariances in the original input data) (Hair et
al. 1998).
Joreskog and Sorbom (1988) proposed that if the value of the modification index (MI) on a coefficient
value is equal to or greater than 3.84. chi-square can be statistically significantly reduced with
the estimation ofthe coefficient. In other words, MI is a measure of any items loaded on multiple factors.
Fromamoreconservativeperspective, ifthevalueof MI is equal to or greater than ten, the estimation
of a coefficient may be considered (Fassinger 1987). In the pretest, the minimum value of 3.84
suggested by Joreskog and Sorbom (1988) was adopted in consideration of: (1) eliminating as
many poor measurement items as possible, and (2) the practical necessity of reducing as many
questionnaire items as possible for the final test. In the final test, however, Fassinger's conservative
approach was adopted with the suggestion that MI can be seriously considered for making decisions
of deletion of any bad indicators and/or refitting the hypothetical model only if a theoretical justification
exists (Hair etal. 1998;Loehlin 1998; MacCullum 1986). In the final te.st, therefore, coefficients
with the MI value of ten or greater were closely reviewed based on an assumption that most of the
multi-loaded items had already been screened out in the pretest.
It should be noted that MI was not the only basis of elimination of any indicators, nor is MI only
for elimination decisions. Instead, the decisions for deleting any bad indicators and/or refitting the
hypothetical model elimination decisions were based upon the theory with the help of MI, qualitative
assessments (i.e., content and face validity tests), and refitting the model itself As a part of theoretical
justification, for example, content/face validity assessed whether: (1) the items were consistent
with the theoretical domain ofthe construct; (2) the items wererepresentativeof the constructs the#p#分页标题#e#
items were proposed to measure; and (3) the items were not difficult, ambiguous, or double-barreled
statements. As such, various efforts were made - such as refitting the model and qualitative assessment
- before an item was finally removed from the model.
Second, concerning the overall assessment ofthe hypothesized model adequacy, overall model
fit indices such as the ratio of x^ to degrees of freedom (CMIN/DF), Goodness of Fit (GFI), Adjusted
72 MIN AND MENTZER
Goodness of Fit (AGFl), Comparative Fit Index (CFI), and Root Mean Square of Approximation
(RMSEA) were used in this study. Conceming the acceptable fit between the hypothetical model and
the sample data (i.e., CMIN/DF value), a rado of equal to or less than I indicates die hypothetical model
is overfitted (Hair et al. 1998), and a ratio of 2 to 1 or 3 to 1 shows an acceptable hypothetical
model (Carmines and Mclver 1981). GFI represents the degree to which the actual or observed
covariance matrix is predicted by the estimated model (Hair et al. 1998). GFI deals with explained
covariance relative to total covariance (Loehlin 1998). GFI values can range from 0.0 (poor fit) to 1.0
(perfect fit) (Hair et al. 1998; Loehlin 1998). In practice, a GFI value greater than 0.9 represents a strong
fit (Bentler and Bonett 1980; Cuttance 1987; Hu and Bentler 1995). AGFI is an extension of GFI,
which is adjusted by the ratio of degrees of freedom for the proposed model to the degrees of freedom
for the null model (Hair et al. 1998). Hair et al. (1998) suggested an AGFI of equal to or greater than
0.9 indicates a good fit and an AGFI that is greater than 0.8 is a sign of marginal fit. CFI, a populationbased
version of the normed fit index (NFI) or a relative comparison of the proposed model to the null
model, is truncated to fall in the range from 0 to 1 (Arbuckle and Wothke 1995; Loehlin 1998). Bentler
(1990) proposed the minimum value ofO.9 is indicative of good model fit. However, Loehlin (1998)
proposed the valueofCFIof less than, but close to, 0.9 isalsoappealing.Therefore, aCFI valueof
about 0.9 or greater was utilized as a barometer ofa good model fit in this study. RMSEA represents
the square root of the ratio of the rescaled noncentrality index (i.e., the population discrepancy
function) to the model's degrees of freedom (Arbuckle and Wothke 1995). In other words, RMSEA
is the discrepancy [>er degrees of freedom measured in terms of the population (Hair etal. 1998) and,
so, is relatively insensitive to sample size (Loehlin 1998). Browne and Cudeck (199.3) sugge.sted a value
of RMSEA of about 0.05 or less indicates a close fit of the model in relation to the degrees of freedom,
and a value of about 0.08 or less for RMSEA indicates a reasonable error of approximation.
Third, once the hypothesized model was "purified," the refined measurement model was put to#p#分页标题#e#
the test of unidimensionality and validity through the CFA provided by the AMOS program. Conceming
unidimensionality, critical ratios (CR) of regression weights of the measured variables and
the latent variables were examined lo see if the regression weights were significant (i.e., > I.% at 0.05
significance level) (cf. Gerbing and Anderson 1988). CRs are obtained by dividing the estimate by its
standard error (i.e., SE), and CR tests the null hypothesis that, in the population, the regression
coefficient is zero. Regarding the tests of convergent validity and discriminant validity, Widaman's
(1985) three comparison models were adopted in this study: Model 0 with individual measurement
items as unique factors in a construct; Model I with individual items loaded on one unique first order
factor, and Model 2 with individual items loaded on any one of the appropriate first order factors that,
in turn, are loaded on the second order factor (see Figure 2 for an illustration). According to several
authors (Bienstock, Mentzer, and Bird 1997; Mentzer, Flint, and Kent 1999; Widaman 1985), significant
x^ statistics in the comparison of Model 0 with Model 1 suggest convergent validity, and in
the comparison of Model 1 with Model 2 provide evidence of discriminant validity. The comparison
result of Model 1 and Model 2 also indicate.^ whether the construct should fit as a first order factor or
a second order factor.
JOURNAL OE BUSINESS LOGISTICS. Vol. 25. No. 1.2004 73
EIGURE 2
WIDAMAN'S THREE COMPARISON MODELS
(A SIMPLIFIED EXAMPLE USING THE SCO MEASUREMENT MODEL)
Mode] 0 Model 1 Model 2
Finally, a test of internal consistency reliability was performed through Cronbach's alpha (i.e.,
the reliability coefficient). Cronbach's alpha is an estimate of internal consistency of responses
from multiple items, based on the average inter-item correlation (Malhotra 1993). Nunnally (1978)
suggested equal to or greater than 0.7 for the reliability coefficient indicates a satisfactory level of reliability
for widely used scales. Others (e.g.. Hair et al. 1998; Loehlin 1998) propose, however, values
below 0.7 are considered still acceptable if the research is exploratory in nature (i.e.. one of few empirical
studies available in the literature). A bivariate correlation was reported for two-indicator factors
due to the inability of calculating all possible split-half reliability coefficients.
SUPPLY CHAIN ORIENTATION SCALE
Throughout the scale development process, a large pool of the SCO items was reduced to 20
items: Credibility was measured with 4 measurement items. Benevolence with 4 items. Commitment
with 2 items. Cooperative Norms with 3 items. Compatibility with 2 items, and Top Management Support
with 5 items. The best altemative model showed a reasonably good model fit (CMIN/DF = 2.275,
GFI = 0.883, AGFI = 0.850, CFI = 0.922, RMSEA = 0.065) (Table 2).#p#分页标题#e#
74 MIN AND MENTZER
TABLE 2
MODEL FIT INDICES
CMIN
DF
CMINDF
GFI
AGFI
CFI
RMSEA
SCO
373.118
164
2.275
0.883
0.85
0.922
O.Oft.'i
SCM
830.829
398
2.088
0.847
0.822
0.924
0.06
Performance
126.382
60
2.106
0.942
0.912
0.946
0.061
Structural Model
3,812.714
1,870
1.702
0.751
0.731
0.884
0.048
The SCO scale was examined for unidimensionality through CFA (Table 3). Critical ratios of
regression weights ofthe items were significant (i.e., > 2) for the first and the second order factors and,
thus, unidimensionatity for each factor was found to exist. Construct validity (i.e., convergent validity
and discriminant validity) of the SCO scale was examined through CFA, using Widaman's
(1985)threecomparison models. As shown inTable4, the comparison of Model 0 and Model 1 provided
evidence of convergent validity: x '= 1319.12 at Df"= 20, and the comparison of Model 1 with
Model 2 provided evidence of discriminant validity: x ' = 1' 67.232 at DF = 6.
TABLE 3
SEM ESTIMATES. CRITICAL RATIOS, STANDARDIZED ALPHA, CORRELATIONS
SCO MEASUREMENT MODEL (FINAL TEST)
Path
Standardized Critical Standardized [(em-Total
Weight Ratio Alpha Correlation Note
CRED •*-
BENE -^
COMM-*-
NORM •*-
COMP •*-
TOP <-
Credl •*-
Cred2 '*-
Cred3 •*-
Cred4 •*-
Bene1 -^
Bene2 •-
Bene3 •*-
Bene4 •*-
SCO
SCO
SCO
SCO
SCO
SCO
CRED
CRED
CRED
CRED
BENE
BENE
BENE
BENE
0.697
0.468
0.816
0.889
0.796
0.566
0.722
0.7.54
0.714
0.581
0.663
0.822
0.879
0.816
7.254
5.615
7.061
(Fixed)
5.645
7.404
10.954
(Fixed)
10.874
9.079
12.282
(Fixed)
17.061
15.534
N/A N/A
0.7862 0.6076
0.6280
0.6166
0.5102
0.8719 0.6236
0.7639
0.7880
0.7326
JOURNAL OF BUSINESS LOGISTICS, Vol. 25. No. 1,2004 75
TABLE 3 (CONT.)
SEM ESTIMATES, CRITICAL RATIOS, STANDARDIZED ALPHA, CORRELATIONS
SCO MEASUREMENT MODEL (FINAL TEST)
Path
Comml
Comm3
Norml
Nomi2
Norm 3
Compl
Comp2
Topml
Topm2
Topm3
Topm4
Topm5
•*- COMM
'*- COMM
•*- NORM
•*- NORM
-^ NORM
^ COMP
^ COMP
<- TOPM
-*- TOPM
•*- TOPM
-*- TOPM
•^ TOPM
Standardized#p#分页标题#e#
Weight
0.686
0.491
0.576
0.690
0.643
0.873
0.540
0.624
0.965
0.951
0.559
0.471
Critical
Ratio
(Fixed)
5.889
7.277
9.135
(Fixed)
7.185
(Fixed)
13.225
(Fixed)
34.185
11.028
8.934
Standardized
Alpha
N/A
0.6612
N/A
0.8447
Item-Total
Correlation
0.337*
0.3900
0.5625
0.4742
0.471*
0.5879
0.8111
0.7958
0.5798
0.4930
Note
0.01 level(2-iailed)
0.01 level (2-tailed)
*Two-item factor.
TABLE 4
CONVERGENT AND DISCRIMINANT VALIDITY TESTS
Model 0
X'ti
DFn
Model I
Model 2
xh
DFi
ModelO-l
x^o - x^i
Df0 - DF\
Model 1 - 2
X^ - X^2
DFi-DF2
SCO
2859.47
190
1540.35
170
373.118
164
1319.12
20
1167.23
6
SCM
6108.75
435
2224.16
4O.'i
832.320
398
3884.59
30
1391.84
7
Performance
2.361.56
78
1033.37
65
126.38
60
1328.19
13
906.99
5
76 MIN AND MENTZER
The intemal consistency reliability ofthe first order factors was tested through Cronbach's alpha
or bivariate correlation and the summary results are reported in Table 3. The standardized Cronbach's
alpha for Credibility, Benevolence, Cooperative Norms, and Top Management Support exceeded either
the Nunnally (1978) or the Hair etal. (1998) and Loehlin (1998) criterion (i.e., 0.7862,0.8719,0.6612,
and 0.8447 respectively). Regarding Commitment and Compatibility, both of which were two-item
factors, the correlation was significant at the 0.01 level. O12 (NORM 1), "Our business unit is willing
to make cooperative changes with our supply chain members," may not belong to the domain of
Cooperative Norm, i.e., a relatively low value of item-to-total correlation (i.e., 0.39). When 012 was
removed, however, the gain in alpha value was minimal (i.e., 0.6636 - 0.6600 = O.(X)36) and, thus, O12
was kept to measure Cooperative Norms. Overall, the standardized alpha for ail the SCO measurement
items was 0.8766 and, thus, it was concluded the SCO measurement items passed the reliability test.
Supply Chain Management Scale
Thirty out of 42 potential indicators of the SCM construct survived the scale development
process: Agreement on Vision and Goals with 4 measurement items. Information Sharing with 3 items.
Risk and Reward Sharing with 3 items. Cooperation with 6 items. Process Integration with 5 items,
Long-Temi Relationship with 3 items, and Agreement on Supply Chain Leadership with 6 items. The
best altemative model had a reasonable fit in temis of overall model fit indices (i.e., CMIN/DF = 2.091,#p#分页标题#e#
GFI = 0.847, AGFI = 0.822. CFI = 0.923, RMSEA = 0.060) (Table 2).
The test of unidimensionality of the SCM scale with the final estimates of the regression
weights, standard errors, and the critical ratios is provided in Table 5. Critical ratios of regression
weights of the items were significant (i.e., > 2) for every first and second order factors. Unidimensionality
for each factor of SCM was, therefore, concluded to exist. The comparison results of
Model 0 and Model 1 ledtoaconclusionof convergent validity (i.e.. x^ = 3884.59 at/?F= 30), and
the comparison of Model 1 withModeI21edtoaconc!usion of discriminant validity (i.e.,x^= 1391.84
at DF = 7) (Table 4).
JOURNAL OF BUSINESS LOGISTICS, Vol. 25, No. 1.2004 77
TABLE 5
SEM ESTIMATES, CRITICAL RATIOS, STANDARDIZED ALPHA, CORRELATIONS
SCM MEASUREMENT MODEL (FINAL TEST)
Path
Standardized
Weight
Critical
Ratio
Standardized
Alpha
Uem-Totai
Correlation Note
VISN •*-
INFO •*-
RISK •*-
COOP -^
INTG -^
REL •*-
LEAD •*-
Visnl *-
Visn2 ••
Visn3 ^
Visn4 *-
SCM
SCM
SCM
SCM
SCM
SCM
SCM
VISN
VISN
VISN
VISN
0.736
0.625
0.903
0.936
0-974
0.860
0.599
0.827
0.900
0.900
0.864
11.770
9.943
11.321
(Fixed)
IL476
L3.I63
9.032
(Fixed)
0.045
19.909
10.083
N/A N/A
0.8608
Coop]
Coop2
Coop3
Coof>4
Coop5
Coop6
Intgl
Intg2
Intg3
Intg4
Intg5
•^ COOP
• - COOP
• • COOP
•*- COOP
•*- COOP
•^ COOP
•*- INTG
•*- INTG
•«- INTG
•*- INTG
•*- INTG
0.791
0.809
0.816
0.748
0.562
0.697
0.672
0.596
0.692
0.594
0.769
(Fixed)
15.290
15.313
13.795
10.0CX)
12.779
10.7S5
9.734
(Fixed)
9.552
12.190
0.8770
0.7469
0.8179
0.7796
0.5183
Infol
Info2
Info3
Riskl
Risk2
Risk3
• - INFO
•*- INFO
•*- INFO
*- RISK
•^ RISK
*- RISK
0.380
0.915
0.914
0.74S
0.738
0.791
6.658
(Fixed)
19.072
(Fixed!
11.619
12.409
0.7486
0.8023
0.3424
0.7478
0.7211
0.5779
0.6775
0.6997
0.7139
0.7109
0.7700
0.7156
0.5400
0.6419
0.7979
Leadl
Lead2
Lead3#p#分页标题#e#
Lead4
Lead5
Lead6
•*- LEAD
•*- LEAD
•*- LEAD
•*- LEAD
•^ LEAD
*• LEAD
0.648
0.685
0.828
0.818
0.880
0.818
12.042
12.733
16.527
16.354
18.402
(Fixed)
0.9020
0.5512
0.5291
0.6207
0.5183
0.6764
Rel2
Rel3
Rel4
•*- REL
•*- REL
•*- REL
0.736
0.881
0.839
15.022
(Fixed)
17.900
0.8578 0.6814
0.7839
0.7318
0.6099
0.6576
0.7873
0.7598
0.8254
0.7509
78 MIN AND MENTZER
The internal consistency of the first order factors of SCM was assessed through reliability
analysis (Table 5). The standardized Cronbach's alpha for all the first order factors exceeded the threshold
value (i.e., 0.7). S5. "Our supply chain members practice Electronic Data Interchange (EDI), either
via VAN or Internet" may be problematic due to a low item-to-total correlation (i.e., 0.3424). The reliability
test also indicated a possible improvement in the alpha value if S5 was removed (i.e., 0.9117).
In practice, however, the use of EDI may be an important indicator of interfirm information sharing
and, thus, S5 was kept as an indicator of information sharing. The standardized alpha value for all the
SCM measurement items was 0.9511. Thus, it was concluded the SCM scale fulfilled the reliability
test.
Business Performance Scale
According to Walker and Ruekert (1987), business performance is a multidimensional construct.
Authors (e.g., Bowersox andCloss 1996; Cooper and Ellram 1993; Monczka, Trent, and Handfield
1998) agree on the ultimate goal of SCM: to increase supply chain competitiveness (or competitive
advantage). Specifically, SCM pursues: (1) lowering the total amount of resources required to provide
the necessary levei of customer service to a specific segment (e.g.. Cooper and Ellram 1993;
Houlihan 1985; Jones and Riley 1985), and (2) improving customer service through increased
product availability and reduced order cycle time (Cooper and Ellram 1993). Partnerships have the
potentialbenefitsofeliminatingredundant pools of inventory and duplicate service operations and,
therefore, reducing costs (Narus and Anderson 1996). Effective customer service includes such
critical factors as availability, variety of product/service offerings, and timeliness (cf Bienstock,
Mentzer, and Bird 1997; Min and Keebler2(K)l), Assuch, SCM is concerned with improving both
efficiency (i.e., cost reduction) and effectiveness (i.e., customer service) in a strategic context (i.e., creating
customer value) to obtain improved competitiveness (i.e., growth) that, ultimately, brings
profitability.
Researchers have taken both subjective and objective approaches to measure business performance#p#分页标题#e#
and found a strong correlation between subjective and objective responses (Dess and Robinson
1984; Robinson and Pearce 1988; Venkatraman and Ramanujam 1986). Also following the
literature (Matsuno and Mentzer 2000; Matsuno, Mentzer, and Ozsomer 2(X)2; Matsuno, Mentzer, and
Rentz 2000), comparison measures (i.e., business pertormance relative to major comfietitors) were
adapted to give respondents an anchor point that would help respondents assess the level of the firm
performance in a more objective manner.
Ba.sed upon the literature review, as well as CFA analysis, business performance was measured
with five first order factors - Availability, Variety of Product^Service Offerings, Timeliness, Profitability,
and Growth. The best measurement model with 13 measurement items, after a careful CFA iteration
process, revealed a good fit in terms of overall model fit indices (i.e., CMIN/DF = 2.106, GFI = 0.942,
AGFI = 0.912, CFI = 0.971. RMSEA = 0.061) (Table 2).
Unidimensionality of the business performance scale was established based upon the final
estimates ofthe regression weights, standard errors, and the critical ratios summarized in Table 6. The
JOURNAL OF BUSINESS LOGISTICS. Vol. 25, No. 1,2004 79
significant x^ statistics provided by Widaman's method verified both convergent and discriminant
validity: x^= 1328.197 at D f = 13 (Model 0-Model 1), and x"^ = 906.985 at DF = 5 (Model 1 -
Model2)(Table4).
TABLE 6
SEM ESTIMATES, CRITICAL RATIOS, STANDARDIZED ALPHA, CORRELATIONS
BUSINESS PERFORMANCE MEASUREMENT MODEL (FINAL TEST)
Path
Standardized
Weigh!
Critical
Ratio
Standardized
Alpha
Item-Total
Correlation Note
AVAI •-
p&s •*-
TIME -^
PROE •*-
GROW •*-
PERF
PERE
PERE
PERE
PERF
0.604
0.689
0.655
0.665
0.623
6.614
5.783
6.963
(Fixed)
7,417
Avail
Avai2
P&SI
P&S2
P&S3
Timel
Time2
Time3
ProfI
Prof2
Prof3
Grow2
Grow3
-*- AVAI
•*- AVAI
-*- P&S
•*- P&S
•*- P&S
•*- TIME
•*- TIME
• ^ TIME
•*- PROF
• ^ PROE
•*- PROE
-^ GROW
•^ GROW
0.826
0.931
0.554
ii.lll
0.495
0.752
0.904
0.591
0.932
0.969
0.889
0.927
0.898
(Fixed)
11.139
(Fixed)
7.013
6,144
(Fixed)
12.379
9,777
33.4S7
(Fixed)
28.254
(Fixed!
14.205
N/A
0.6303
0.787
0.9497
N/A
0,768*
0.4217
0.5087
0.3786
0.6505
0.7060#p#分页标题#e#
0.5241
0.8968
0.9235
0.8632
0.833*
0,01 level(2-tailed)
0.01 level (2-tailed)
* Two-item factor.
Internal reliability was assessed through either Cronbach's alpha or bivariate correlation {Table
6). The standardized alphas for Variety of Product/Service Offerings, Timeliness, and Profitability met
either the Nunnally (1978) or the Hair et al. (1998) and Loehlin (1998) criterion (i.e., 0.6303,
0.7870, and 0.9497 respectively). The bivariate correlations for Availability and Growth were significant
at the 0.01 level. Taken as a whole, Cronbach's alpha for the business performance construct
was 0.8652. The reliability coefficient for each first order factor indicated either an acceptable or marginal
level of reliability and, therefore, the business performance scale passed the reliability test.
80 MIN AND MENTZER
NOMOLOGICAL VALIDITY TEST
As the measurement models were validated in SEM, a structural model was estimated, which is
the procedure for empirical estimation ofthe strength of each relationship between exogenous (i.e.,
a SCO) and endogenous (i.e., SCM and performance) variables depicted in the theory (Figure I). In
other words, the structural model tests the theory that the SCO-SCM path has a positive impact on
business petformance of individual firms in a supply chain (Mentzer et al. 2001). The estimation of
the structural model was necessary to test the nomological validity ofthe SCO, SCM, and performance
constructs, which is the extent to which measures of different but related constructs correlate to each
other in theoretically predicted ways (cf. Garver and Mentzer 1999; Malhotra 1993).
Before nomoiogical validity was assessed with the structural model, however, a comparison
model test was pertbrmed to assure the SCM concepts - SCO and SCM - are in fact closely related
but two different concepts: Model A with a SCO and SCM as two correlated but distinctive second
order factors, and Model B withonesecondorderfactorto which all the first order factors ofthe SCO
and SCM constructs are converged. The comparison results showed Model A exhibited better fit than
Model B (Table 7). Therefore, the SCO and SCM concepts are related but two different concepts, based
upon the theory and the empirical test.
TABLE 7
DISTINCTIVENESS BETWEEN SCO AND SCM
Model A - Two Model B - One Model
Construct Model Construct Model Comparison
X^ 2186.02 2450.49 264.47
DF 1161 1163 2
The structural model in which the SCO-SCM-performance path was posited provided a reasonable,
acceptable fit (i.e., CFI = 0.884, Tucker-Lewis Index (TLI) = .879, RMSEA = 0.048),
especially when the complexity of the model (i.e., total disaggregation model) was considered
(Table 2). First, the structural model in this paper is a total disaggregation model (with 63 manifest variables)#p#分页标题#e#
that generally fits data poorer in terms of GFI and AGFI indices (i.e., 0.751 and 0.731 respectively)
than partial disaggregation or aggregation models (Bagozzi and Heatherton 1984; Leone et al.
2(X)I). Thus, Leone etal. (2(X)!)recommendedTLIandCFI(bothof which are relative fit indices)
to assess model fit of total disaggregation models, and RMSEA (which is an absolute fit assessing badness
of fit per degree of freedom). Our argument of a "reasonable, acceptable fit," although not a satisfactory
fit. is based upon a CFI value that is .884 (close to .9) (cf. Loehlin 1998), a TLI value that is
.879 (close to .9) (cf. Hair et al. 1998), as well as an RMSEA value that is .044, which is far below the
suggested threshold (i.e., .08) (cf. Browne and Cudeck 1993). Further examination ofthe structural
JOURNAL OF BUSINESS LOGISTICS. Vol. 25, No. 1,2004 81
model involves the significance of estimated regression weights tested with a critical ratio (i.e., > 1.%).
As suggested by Mentzer et al. (2001), there existed a positive SCO-SCM path (i.e., CR = 5.794, which
is significant at the O.(X)1 level) (Table 8). In addition, the positive SCM-business performance
path was evidenced by the statistically significant standardized regression weight on the path (i.e.,
CR = 3.821, which is significant at the 0.001 level) (Table 8), Thus, we conclude the positive
impacts of the SCO-SCM linkonperformanceexistand, thus, the nomological validity of the SCO
and SCM scales was supported.
TABLE 8
FINAL SEM ESTIMATES, STANDARD ERRORS, AND CRITICAL RATIOS
STRUCTURAL MODEL
SCM
PERF
AVAI
P&S
TIME
PROF
GROW
CRED
COMM
NORM
COMP
TOP
VISN
INFO
RISK
COOP
I^4TG
REL
LEAD
BENE
•*- SCO
•*- SCM
•*- PERF
• ^ PERF
• ^ PERF
• ^ PERF
• ^ PERF
*• SCO
*• SCO
•«- SCO
•- SCO
•- SCO
• • SCM
•*- SCM
•*- SCM
• - SCM
•*- SCM
•^ SCM
•*- SCM
*• SCO
Std.
Regression
Weight
0.613
0.280
0.613
0.684
0.651
0.666
0.617
0.609
0.729
0,819
0,865
0,607
0,755
0.614
0.897
0.931
0.976
0.865
0.596
0.560
Std.
Error
0.249
0.048
0.12
0.105
0,129
0.129
0.186
0.19
0.224
0.26
0.076
0.092
0.087
0.091
0.086
0,09
0.187
Critical
Ratio
5.794
3.821
6.443
5.727
7.407
(Item fixed)
7,776
(Item fixed)
4.595
6.67#p#分页标题#e#
5.784
6.737
11.125
9.229
10.838
11,579
(Item fixed)
12,204
9.022
5.79H
P
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Note: Only first order factors and second orders appeared in this table.
CONCLUSIONS
The goal of this study was to operational ize the constructs of a supply chain orientation and
supply chain management. The developed measurement scales of a SCO and SCM satisfied the criteria
of the tests of unidimensionality, construct validity, and intemal consistency reliability through
82 MIN AND MENTZER
http://www.ukassignment.org/liuxueshenglunwen/a series of statistical analyses. In addition, the suggested structural model supported the nomological
validity of the SCO-SCM path improving business performance of individual firms.
The findings have both managerial and research implications. For managers who are involved
in supply chain management, this study offers a clear conceptualization of supply chain management
in that a supply chain orientation is an Implementation of the supply chain philosophy that should hapfjen
inside individual firms, and supply chain management is the sum total ofall the overt management
actions undertaken to realize that philosophy across firms. The clear-cut conceptualization of a
SCO and SCM, and their effect upon business performance, is strong support for the effectiveness of
SCM in the pursuit of competitive advantage.
Further, the sp)ecifics of implementing both a SCO within a firm and SCM acnsss firms were documented,
including the activities to be performed, consequences to be expected, and organizational
as well as functional boundaries. The measurement scales developed in this study can be utilized as
a managerial assessment tool at a strategic level. First, with our framework, managers should be able
to carry out the necessary managerial actions to implement a SCO and SCM. In addition, managers
should realize that the implementation of SCM with other firms in the supply chain should be
preceded by a SCO inside their firms.
Second, either during and/or after the implementation of a SCO and SCM, managers can use the
measures of a SCO and SCM provided in Appendix I to evaluate their progress toward a SCO and
SCM. If there is any gap between expectation and perfomiance, managers should take actions to reduce
the gap for effective implementation of a SCO and/or SCM. The evaluation may be performed as a part
of diagnostics for process reengineering at a company level and/or a joint SCM audit at a supply chain
level across partnering firms within the supply chain.
Third, managers should also be constantly mindful of the finding that SCM activities pay off economically.#p#分页标题#e#
Mentzer etal. (2001)proposed that a successful implementation of SCM improves business
performance of participating firms and the supply chain as a whole. This paper confirmed the
SCO-SCM path in fact leads to improved business performance of individual firms within a supply
chain.
Fourth, it should be noted that the Council of Logistics Management (2002) defines logistics as
"that part of the supply chain process." Although logistics has taken and will continue to take a leading
role in studying and implementing supply chain management, using its unique position of
involvement in overseeing both upstream (i.e., supply) and downstream (i.e., distribution) of the supply
chain process, logistics should be strategically integrated with other functions inside the firm and
across the firms within a supply chain.
Fifth, as verified in this paper, each participating firm must have strong top management support
as well as compatible corporate culture inside the firm for successful implementation of SCM
across firms. That is, company-wide training sessions are necessary to share the importance and characteristics
of actuating SCM activities within a supply chain. Therefore, the results of this paper should
JOURNAL OE BUSINESS LOGISTICS, Vol. 25, No. 1.2004 83
be a good addition to in-house management training materials on the subject of supply chain
management.
Finally, a caution should be issued that the concepts, a SCO and SCM, are strategic in nature and,
thus, our scales are not intended to provide a detailed activity list for implementing a SCO and
SCM for a day to-day operation at an operational level. In other words, although managers should benefit
from our scales to clarify which strategic level activities should be actualized within the individual
firms and across the firms within the supply chain, managers are encouraged to be creative to come
up with specific, everyday activities that fit in the strategic level implementation of SCO and SCM.
Nevertheless, the framework offered in this paper should function as a management guideline,
helping managers detect and fix any deficiencies in their SCM-related practices.
There are also several implications for researchers. First, the conceptualizations of a SCO and
SCM have been operational ized. The definition of SCM has differed by different authors, creating considerable
confusion for the research community. However, the Mentzer and Kahn (1995) framework
for logistics research calls for a review of the literature and observation of the phenomenon to
develop theoretical meaning, and then empirical testing of the theory. Mentzer et al. (2001) and
Mentzer (2(X)1) provided the literature review and observation (through qualitative executive interviews)
ofthe phenomena that led to the conceptualization of the constructs of SCO and SCM. This
study completes the empirical testing part ofthe Mentzer and Kahn (1995) process with resfiect to SCO#p#分页标题#e#
and SCM. Operationalizing these constructs provides scales which future research can use, refine, and
replicate. Further, well defined and tested constructs (i.e., SCO and SCM) facilitate communication
among researchers because the meanings of the concepts so defined are less subject to misinterpretation(
cf Kerlinger 1986; Underwood 1957).
Second, structural equation modeling was used and confirmed the a priori model as proposed
in the belief that an analysis using SEM should be confirmatory in nature - i.e., to confirm/disconfirm
a proposed model and/or for hypothesis testing (cf. Kelinger 1986). However, in a cross-sectional study
such as this one, we could not sort out all possible causal orders and, thus, the structural model
we tested was based on the a priori model as presented before data were collected. To further test the
a priori model proposed in this study, future research should rigorously develop and empirically test
altemative theoretical modeh.
Third, the findings responded to Mentzer et al.'s (2001) call for empirical research to test the
antecedent, phenomenon, and consequence structure of SCM, and verified the role of a SCO within
a firm and SCM within a supply chain as improving a firm's business performance. Nevertheless, in
consideration ofthe limited scope of this study, this study should only be the first of many future studies
of the SCM phenomenon. For example, although Mentzer et al. (200!) suggest the impact of SCM
is on both the performance of individual supply chain members and supply chains as a whole, only
individual performance was measured in this study. Therefore, future research should operationalize
performance measures of a supply chain, and investigate the relationship between SCM and business
performance of a supply chain as a whole.
84 MIN AND MENTZFR
Fourth, although this study developed a set of indicator variables of supply chain orientation, supply
chain management, and business perfomiance, we acknowledge that some of these indicators need
further refinement. For example, several two-item measures in the tested model should be expanded
to further explore the domain ofthe underlying construct. In addition, several indicator variables that
showed relatively low item-to-total correlations in reliability tests should be refined to be more
congruent to other variables measuring the same latent variables. Therefore, it is hoped that future
research will follow this study to refine the suggested indicator variables, add additional indicator variables,
and further investigate the relationships among the SCM-related concepts.
Finally, as theorized, a positive, significant regression coefficient was found on the SCM -*•
Business Perfomiance path during the nomological validity test (i.e.. SMC = 0.28). Although this finding
supports the claim that a successful implementation of SCM brings to participating firms efficiency#p#分页标题#e#
and effectiveness toward competitive advantage, possible causes of such a statistically valid, but small,
effect should be discussed. A possibility is that although theory suggests successful SCM improves
overall performance of the firm and the supply chain as a whole, the implementation of SCM within
the supply chain may conlribute more to the performance of a supply chain than to individual firms.
The other possibility is, as Mentzer et al. (2001) suggested, well executed SCM helps improve the longlerm
peifoi'miince of the individual firms and the supply chain as a whole. Unfortunately, the business
performance of the supply chain as a whole, as well as long-term evaluation of business performance
of both individual firms and ihe supply chain, was outside the scope of this study and, thus, future
research is called for to explore these possibilities.
In conclusion, it should be emphasized that this study was a first effort to develop SCM related
scales. The robustness of the measurement items in terms of validity and reliability and/or the
strengths ofthe relationships between the latent variables can always stand improvement. Therefore,
future research is called for to continuously refine the measurement scales and strengthen the findings
of this study.
' Mentzer et al. (2001) defined a SCO as "the recognition" of the SCM philosophy although they emphasized
embracing the philosophy wilhin a firm. BecauseaSCOisaseiof implementation behaviors and, accordingly.
is measured on a behavioral scale, a SCO should be defined as "the implementation" by an organization of the
SCM philosophy.
^ Mentzer et al. {2001) posiled these five construcis as antecedents of a SCO. It is proposed in this paper, however.
that these elements reflect the sysiemic and strategic view toward customer value creation and, thus, are indicators
of the degree of a SCO within the boundaries ofa firm.
^ Mentzer el al. (2001) posited supply chain leadership as a prerequisile of a SCO. Since acknowledging a
supply chain leader requires wide acceptance from the supply chain members, it is proposed here thai agreement
on supply chain leadership is an element of implementing SCM across supply chain members.
JOURNAL OF BUSINESS L OGISTICS. Vol. 25, No. 1,2004 85
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ABOUT THE AUTHORS
Soonhong (Hong) Min (Ph.D., University of Tennessee) is an Assistant Professor of Marketing
and Supply Chain Management at The University of Oklahoma. He has published in the Journal
of Business Logistics, Journal of Retailing, International Journal of Physical Distribution and
Logistics Management, International Marketing Review, Journal of Marketing Theory and Practice,
and the E-Bu.^iness Review and co-authored the book. Supply Chain Management.
John T. (Tom) Mentzer (Ph.D., Michigan State University) is the Hany J. and Vivienne R. Bruce
Excellence Chair of Business in the Department of Marketing, Logistics and Transportation at the University
of Tennessee. He has published five books and more than 170 articles and papers in the
Journal of Business Logistics, Journal of Marketing, Journal of Business Research, International Journal
of Physical Distribution and Logistics Management, Transportation and Logistics Review,
Transportation Journal, Journal of the Academy of Marketing Science. Columbia Journal of World
Business, Industrial Marketing Management, Research in Marketing, Business Horizons, and other
joumals.
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