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Research paper

The impact of logistics performance onorganizational performance in a supply chain

contextKenneth W. Green Jr

College of Business Administration, Department of Management and Marketing, Sam Houston State University, Huntsville, Texas, USA

Dwayne WhittenTexas A&M University, Mays School of Business, Information and Operations Management Department, College Station, Texas, USA,

and

R. Anthony InmanCollege of Administration and Business, Louisiana Tech University, Ruston, Louisiana, USA

AbstractPurpose – The paper’s aim is to theorize and assess a logistics performance model incorporating logistics performance as the focal construct withsupply chain management strategy as antecedent and organizational performance, both marketing and financial, as consequences.Design/methodology/approach – Data from a national sample of 142 plant and operations managers are analyzed using a structural equationmodeling methodology.Findings – The results indicate that logistics performance is positively impacted by supply chain management strategy and that both logisticsperformance and supply chain management strategy positively impact marketing performance, which in turn positively impacts financial performance.Neither supply chain management strategy nor logistics performance was found to directly impact financial performance.Research limitations/implications – To compete at the supply chain level, manufacturers must adopt a supply chain management strategy. Such astrategy requires integration and coordination of key external processes such as purchasing, selling, and logistics with supply chain partners. In thisstudy the focus is limited to the impact of logistics performance on organizational performance within a supply chain context.Practical implications – As manufacturers work to improve the logistics processes, they support their organization’s supply chain strategy, resultingin improved performance for the overall supply chain and ultimately their manufacturing organizations.Originality/value – Organizational managers are being asked to focus directly on supply chain functions such as logistics to bolster thecompetitiveness of the supply chains in which their organizations are integral partners. Does such a supply chain focus ultimately result in improvedorganizational performance? This study provides evidence that a supply chain focus will enhance logistics performance, which will ultimately result inimproved organizational performance.

Keywords Supply chain management, Organizational performance, Mathematical modelling

Paper type Research paper

Introduction

According to de Kluyver and Pearce (2006, p. 4), the ultimate

goal of strategy is “long-term, sustainable superior

performance.” Such superior performance now depends on

the ability of a manufacturing organization to become a fully

integrated partner within a supply chain context (Cooper

et al., 1997), thus all but requiring that manufacturing

organizations adopt a supply chain strategy. Such supply

chain strategies focus on how both internal and external

business processes can be integrated and coordinated

throughout the supply chain to better serve ultimate

customers and consumers while enhancing the performance

of the individual supply chain members (Cohen and Roussel,

2005). Examples of business processes that must be

integrated include manufacturing, purchasing, selling,

logistics, and the delivery of real-time, seamless information

to all supply chain partners. Managing at the supply chain

level requires a new focus and new ways of managing

(Lambert et al., 1998). Manufacturing managers must learn

to communicate, coordinate, and cooperate with supply chain

partners (Gammelgaard and Larson, 2001).For this study, we adopt the Larson and Halldorsson

(2004) “unionist” perspective on the relationship between

supply chain management and logistics. This perspective

holds that supply chain management incorporates logistics as

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Supply Chain Management: An International Journal

13/4 (2008) 317–327

q Emerald Group Publishing Limited [ISSN 1359-8546]

[DOI 10.1108/13598540810882206]

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a key supply chain focused function (Council of Supply Chain

Management Professionals, 2007). Organizational managers

are asked to focus attention and resources directly on supply

chain functions such as logistics to bolster the competitiveness

of the supply chains. The managers are, however, ultimately

judged on the marketing and financial performance of their

organizations.Does a supply chain focus lead to improved logistics

performance, which, in turn, results in improved

organizational performance? It is our purpose to answer that

question. Building on the works of Schramm-Klein and

Morschett (2006), Wisner (2003), and Bowersox et al.(2000), we theorize a logistics performance model with

logistics performance as the focal construct and supply chain

management strategy as antecedent and marketing

performance (sales and market share growth) and financial

performance (return on investment and profit growth) as

consequences. Data collected from a national sample of US

manufacturers are used to assess the model following a

structural equation methodology.A review of the related literature and discussion of the

theorized model with incorporated hypotheses follow in the

next section. The methodology employed in the study is then

presented. The results of the scale assessment and the

structural equation modeling results follow. The conclusions

section, which incorporates discussions of the contributions of

the study, limitations of the study, suggestions for future

related research, and implications for practicing managers is

in the final section.

Literature review

Mentzer et al. (2001, p. 4) define a supply chain as “a set of

three or more entities (organizations or individuals) directly

involved in the upstream and downstream flows of products,

services, finances, and/or information from source to

customer.” Stank et al. (2005, p. 27) describe supply chain

management as a “strategic level concept.” Ho et al. (2002)

conceptualize SCM as having three core elements:1 value creation;2 integration of key business processes; and3 collaboration.

Based on this conceptualization, they define supply chain

management as follows:

SCM is the philosophy of management that involves the management andintegration of a set of selected key business processes from end user throughoriginal suppliers, that provides products, services, and information that addvalue for customers and other stakeholders through the collaborative effortsof supply chain members (Ho et al., 2002, p. 4422).

Logistics is an important component of supply chain

management (Stank et al., 2005). The Council of Supply

Chain Management Professionals (2007) defines logistics

management as “that part of Supply Chain Management 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.”

Both Stank et al. (2002) and Lin (2006) describe the

importance of integrating the logistics processes of all supply

chain partners to better serve the needs of ultimate customers.

Rodrigues et al. (2005, p. 1) identify logistics as “one of the

largest costs involved in international trade.”

Rabinovich and Knemeyer (2006) identify a new breed of

logistics-related firms: logistics service providers that supportinternet supply chains. These logistics service providers help

internet sellers integrate with the myriad of available logisticsfirms to fulfill customer orders more effectively and efficiently

(Rabinovich and Knemeyer, 2006). Logistics serviceproviders establish relationships with both internet sellers

and third-party logistics providers and integrate the sellingand flow processes throughout the supply chain through the

provision of what Rabinovich and Knemeyer (2006, p. 90)call “hub functionalities.” Vaidyanathan (2005) describes a

similar role for fourth-party logistics providers in moretraditional supply chain configurations such as those that link

manufacturers with ultimate customers. Lai and Cheng(2003) discuss the importance of a supply chain focus on thepart of transport logistics service providers as they function to

link suppliers, manufacturers, sellers, and customersthroughout the supply chain. They argue that transport

logistics service providers must focus on supply chainperformance in addition to organizational performance.

Morash and Clinton (1998) investigated the creation ofcustomer value through the supply chain integration

alternatives of collaborative closeness and operationalexcellence. They illustrated models identifying logistics as

the unifying link intra-organizationally between theproduction and marketing functions and inter-

organizationally between suppliers and customers. Analyzingdata from almost 2,000 firms in the USA, Australia, Japan,

and Korea, they found that efficient supply chains exhibitoperational excellence and responsive supply chains exhibit

collaborative closeness. Japanese and Korean firms were morelikely to integrate supply chains based on operational

excellence, while US and Australian firms were more likelyto integrate supply chains on the basis of collaborativecloseness.

Srivastava (2006) investigated the state of logistics andsupply chain practices in India. He found that, while Indian

managers are well aware of the need to develop supplierpartnerships, integrate and coordinate the flow of goods from

supplier’s supplier to ultimate customer, and shareinformation among supply chain partners, the infrastructure

necessary to facilitate such seamless integration is as yetunavailable. There is pressure in emerging markets to rapidly

adopt logistics and supply chain integration practices in aneffort to compete globally.

Chen and Paulraj (2004) proposed a research frameworkfor supply chain management based upon the “collaborative

advantage” paradigm. The framework incorporatesenvironmental uncertainty, strategic purchasing, information

technology, supply network structure, and logistics integrationas impacting buyer-seller relationships and subsequently

resulting in improved buyer and seller performance.Managers have traditionally focused on improving the

performance of the organizational entity for which they aredirectly responsible. Supply chain management requires anexternal focus in which managers must consider the impact of

organizational strategies on supply chain partners. Attemptsto optimize organizational performance may negatively impact

overall supply chain performance, thus damaging thecompetitive advantage of the chain (Chopra and Meindl,

2004; Meredith and Shafer, 2002).According to Chopra and Meindl (2004), supply chain

performance is optimized only when an “inter-organizational,

Impact of logistics performance on organizational performance

Kenneth W. Green Jr, Dwayne Whitten and R. Anthony Inman


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inter-functional” strategic approach is adopted by all chainpartners. Such an approach maximizes the supply chainsurplus available for sharing by all supply chain members.Meredith and Shafer (2002, p. 261) argue that “if eachsegment of the supply chain is acting in a way to optimize itsown value, there will be discontinuities at the interfaces andunnecessary costs will result. If an integrated view is takeninstead, there may be opportunities in the supply chain whereadditional expense or time in one segment can savetremendous expense or time in another segment.”

Organizational strategies that support supply chainstrategies should strengthen the competitive position of thesupply chain which, in turn, enhances performance of each ofthe individual supply chain partners. While the link fromsupply chain performance is theoretically justified, noempirical evidence related to the link was identified.

Logistics performance model

A decade ago, Morash and Clinton (1997) proposed a schemafor future supply chain research that included transportationand logistics capabilities as the link between supply chainstructure and performance. While Wisner (2003)hypothesized a positive link between logistics strategy andorganizational performance, he did not report data collectionrelated to logistics strategy measurement and did not reportresults related to his hypotheses. Schramm-Klein andMorschett (2006) assessed the relationship between logisticsquality and the organizational performance of firms in theretail sector. It is our purpose to build on the work ofSchramm-Klein and Morschett (2006), Wisner (2003), andMorash and Clinton (1997) by empirically investigating thelink between logistics performance and organizationalperformance in the manufacturing sector. We utilizeWisner’s measure of supply chain management strategy, ameasure of logistics performance from Bowersox et al. (2000),and measures of the marketing performance and financialperformance of the organization from Green and Inman(2005) to collect data from a national sample of plant andoperations managers to support a structural analysis of atheorized logistics performance model. We propose a logisticsperformance model that incorporates logistics performance asthe focal construct with supply chain management strategy asantecedent and organizational performance, both financialand marketing, as consequences. Although the model asproposed is original, it does build upon and extend the worksof Green et al. (2006) and Wisner (2003). The modelincorporates six hypotheses and is illustrated in Figure 1.

Construct definitions

The model incorporates the following constructs:. supply chain management strategy;. logistics performance;. marketing performance; and. financial performance.

A supply chain management strategy requires an end-to-endsupply chain focus that supports integration of businessprocesses such as purchasing, manufacturing, selling, andlogistics throughout the chain for the purpose of providingoptimum value to the ultimate customer/consumer (Cohenand Roussel, 2005; Wisner, 2003). Implementation of such astrategy requires that actions be taken to strengthen

relationships and develop trust among supply chain partnersto facilitate the integration of processes throughout the supplychain from suppliers’ supplier to ultimate consumer/consumer(Cohen and Roussel, 2005; Wisner, 2003). The logisticsperformance construct reflects the organization’s performanceas it relates to its ability to deliver goods and services in theprecise quantities and at the precise times required bycustomers. Bowersox et al. (2000) incorporate performancemetrics such as customer satisfaction, delivery speed, deliverydependability, and delivery flexibility. Marketing performancereflects the organization’s ability to increase sales and expandmarket share as compared to its competition (Green andInman, 2005; Green et al., 2006). Financial performancereflects an organization’s profitability and return oninvestment as compared to its competition (Claycomb et al.,1999; Green et al., 2004; Green and Inman, 2005).

Hypotheses

Vokurka and Lummus (2000) specify the goal of supply chainmanagement as adding value for customers at reduced overallcosts. The added value should be reflected in the cost, quality,flexibility, and delivery components of supply chainperformance (Ho et al., 2002). Oliver and Delbridge (2002)and Bowersox et al. (2000) provide empirical evidence relatedto the impact of a supply chain management strategy onsupply chain performance. Oliver and Delbridge (2002)compared six “high performing” supply chains with six “lowperforming” chains on the basis of four supply chainperformance measures. High performing chains exhibitedfewer incoming defects, fewer outgoing defects, a lowerpercentage of late deliveries to second-tier suppliers and alower percentage of late deliveries from first-tier suppliers.Bowersox et al. (2000) gathered data from 306 senior NorthAmerican logistics executives and categorized the companiesrepresented as either “high achievers” or “average achievers”in terms of supply chain competencies. The high and averageachievers were then compared on the basis of logisticsperformance metrics related to customer service, quality,productivity, and asset management. The high achieversexhibited significantly higher scores for each performancemetric measured. Based on this theoretical justification andthe supporting empirical evidence from Bowersox et al.(2000), hypothesis 1 is stated as follows:H1. A supply chain management strategy is positively

associated with logistics performance.

Wisner (2003) hypothesized supply chain management strategyas a positive predictor of firm performance. Justification for thehypothesis was based on the argument that performanceevaluation of the purchasing and supply management functionswill become closely linked to measures of organizationalperformance such as growth, profitability, and market share(Carter and Narasimhan, 1996). Wisner (2003) surveyed USand European manufacturing and service organizations andstructurally assessed a model that incorporated suppliermanagement and customer relationship strategies asantecedents to supply chain management strategy and firmperformance as a consequence. The link from supply chainmanagement strategy to firm performance was found to bepositive and significant as hypothesized. Additional empiricalevidence is provided by Armistead and Mapes (1993), whocollected data from 38 UK manufacturing organizations. Theymeasured supply chain integration and perceptions of




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manufacturing performance and found them to be highly and

positively correlated. After surveying senior supply and

materials management professionals in the USA, Tan (2002)

concluded that supply chain management practices positively

impact firm performance. Vickery et al. (1999) surveyed CEOs

of firms in the office and residential furniture industry to assess

the relationships among supply chain flexibility measures of

product, volume, launch, access and target market flexibility,

and measures of overall firm performance. They found volume

flexibility to be positively correlated with all measures of

performance. Launch and target market flexibility were

correlated with four of the six measures of performance.

Product flexibility was related only to return on investment,

and access flexibility only to market share. Tan et al. (2002)

collected data from 101 senior managers of US manufacturing

firms to assess the relationship between supply chain

management factors and firm performance measures. They

found that the supply chain characteristics factor was

negatively correlated with average selling price and positively

correlated with overall product quality and overall customer

service levels. Green et al. (2006) surveyed sales managers for

manufacturing firms and found positive links between supply

chain management strategy and both marketing and financial

performance. Based on the theoretical justification and

supporting empirical evidence, the second and third

hypotheses are:

H2. A supply chain management strategy is positively

associated with marketing performance.

H3. A supply chain management strategy is positively

associated with financial performance.

Organizational strategies that support supply chain strategies

should strengthen the competitive position of the supply chain

which, in turn, will enhance performance of each of the

individual supply chain partners. Although no empirically

tested measure of supply chain performance was found,

logistics performance focuses outside the manufacturing

function on the manufacturer/customer relationship, and, as

Bowersox et al. (2000) describe it, logistics performance is a

reflection of supply chain superiority. Lin (2006, p. 257)

contends that logistics service providers work to integrate

“business flow, physical flow, money flow, and information

flow in the supply chain.” Such integration serves to

strengthen the supply chain’s overall ability to delivery value

to the ultimate customer. Shao and Ji (2006, p. 64) assert that

“logistics is the key to making and keeping customers.”

Novack et al. (1992) and Schramm-Klein and Morschett

(2006) argue that logistics performance is a necessary

prerequisite to marketing performance. The logistics

function creates place, time, quantity, and space value,

which are essential to customer satisfaction (Novack et al.,

1992; Sheen and Tai, 2006). Wisner (2003) theorized a

positive association between logistics performance and

organizational performance. Schramm-Klein and Morschett

(2006) hypothesized positive associations between logistics

quality and marketing and financial performance and found

support for the hypotheses in a sample of 262 retailers. Based

upon the theoretical justification and empirical results,

hypotheses 4 and 5 are stated as follows:

H4. Logistics performance is positively associated with

marketing performance.

H5. Logistics performance is positively associated with

financial performance.

While organizational managers must focus attention and

resources on supply chain functions such as logistics, their

primary concern remains improved organizational

performance. Specifically, managers work to improve

marketing performance in terms of sales and market share

growth. The growth of market share and sales growth should

impact financial performance through improved revenue

numbers. Anderson et al. (1994) found that marketing

performance, as measured by customer satisfaction, positively

impacts financial performance, as measured by return on

investment. Green et al. (2006) surveyed sales managers for

manufacturing firms and found a positive link between

marketing performance and financial performance. In their

study of retailers, Schramm-Klein and Morschett (2006,

p. 283) hypothesized that “marketing performance has a

positive effect on company performance” and found that sales

performance positively influenced financial performance.

Based on this theoretical justification and empirical

evidence, hypothesis 6 is stated as follows:

H6. Marketing performance is positively associated with

financial performance.

Figure 1 Theorized logistics performance structural model with hypotheses




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Methodology

Plant and operations managers representing 1,461 different

firms were selected from the Manufacturers, News, Inc.

database of US manufacturers with 500 or more employees.

These manufacturing managers were surveyed using a

traditional initial and follow-up mailing procedure during

the spring of 2005.The sample frame was constructed primarily to target

relatively high-level managers such as plant and operations

managers. Such high-level managers were targeted in the

belief that, while they are intimately aware of the internal

operational workings of their organizations, they are also well

aware of their organization’s supply chain strategy and the

performance of such supply chain functions as logistics. As

the focus of operations related research shifts from the firm to

the supply chain level, it becomes more difficult for

researchers to identify groups within organizations that are

both aware of both internal and external processes and

performance. As more supply chain professionals are

employed by firms, a new more appropriate sample frame

for supply chain research may develop.In total, 142 responded with completed instruments for a

response rate of 9.7 percent. The response rate is not atypical

of that obtained in industrial research (Harmon et al., 2002).

Other reported response rates under similar circumstances

are:. 7.5 percent (Nahm et al., 2004);. 10.8 percent (Harmon et al., 2002); and. 6.7 percent (Tan et al., 2002).

Larson (2005) found that response rates for mail survey-

based studies in the Journal of Business Logistics declined

precipitously during the period from 1989 to 2003. While

manufacturing managers are the prime source for supply

chain management related data, they are often under severe

time and resource constraints.In addition to the survey response rate, item completion

rate can be used as another measure of survey effectiveness

(Klassen and Jacobs, 2001). Klassen and Jacobs (2001,

p. 717) define item completion rate as “the proportion of

survey items answered relative to all applicable items.” The

item completion rate for this study is a relatively high 96.7

percent.In their discussion of sample size necessary to support

structural equation modeling, Hair et al. (2006, p. 742) state,

“SEM models containing five or fewer constructs, each with

more than three items (observed variables), and with high

item communalities (0.6 or higher), can be adequately

estimated with samples as small as 100-150.” The

measurement model illustrated in Figure 2 incorporates four

constructs, each with three or more observed items, all of

which exhibit communalities greater than 0.60. The sample

size of 142 is, therefore, considered adequate to support the

structural equation analysis necessary to assess the logistics

performance model (Hair et al., 2006).All of the respondents indicated that they worked for

manufacturing organizations. In total, 62 percent of the

respondents identified themselves specifically as plant or

operations managers. An additional 15 percent held

purchasing and inventory management positions. A total of

19 specific manufacturing SIC codes were identified and

respondents represented 33 different states.

Non-response bias was assessed using a common approach

described by Lambert and Harrington (1990) in which theresponses of early and late respondents are compared. Of the

study respondents, 54 percent (77) were categorized as early

respondents and 46 percent (65) were categorized as laterespondents based on whether they responded to the initial or

follow-up request to participate. A comparison of the meansof the descriptive variables and the scale items for the two

groups was conducted using one-way ANOVA. Thecomparisons resulted in statistically non-significant

differences. Because non-respondents have been found todescriptively resemble late respondents (Armstrong and

Overton, 1977), this finding of general equality betweenearly and late respondents supports the conclusion that non-

response bias is not a major concern.When data for the independent and dependent variables are

collected from single informants, common method bias may

lead to inflated estimates of the relationships between thevariables (Podsakoff and Organ, 1986). Harman’s one-factor

test was used post hoc to examine the extent of the potentialbias. Substantial common method variance is signaled by the

emergence of either a single factor or one “general” factorthat explains a majority of the total variance (Podsakoff and

Organ, 1986). Results of the factor analysis revealed sevenfactors, which combined to account for 71 percent of the total

variance. While the first factor accounted for 31 percent of thetotal variance, it did not account for a majority of the

variance. Based upon these results, problems associated withcommon method bias are not considered significant.

Results

Measurement of constructs

A 12-item scale developed by Wisner (2003) was used to

measure supply chain management strategy. Respondentswere asked to indicate the importance of the listed issues and

concerns to their organization’s supply chain efforts. Logistics

performance was measured using a 13-item scale developedby Bowersox et al. (2000). Respondents were asked to rate

their organization’s performance compared to that of theircompetitors on the performance metrics related to customer

service, cost management, quality, productivity, and assetmanagement performance metrics. Organizational

performance was measured using two scales previously usedand assessed by Green and Inman (2005). Respondents were

asked to compare their organization’s financial performance(four-item scale) and marketing performance (three-item

scale) to the performance of competitors.Scales such as those incorporated in this study must exhibit

unidimensionality, reliability, and content, discriminant,convergent, and predictive validity (Garver and Mentzer,

1999; Ahire et al., 1996). Because all study scales were

previously developed and assessed (Green and Inman, 2005;Wisner, 2003; Bowersox et al., 2000), content validity is

assumed. Following the standard methodology used by Dunnet al. (1994), refinement of the measurement scales is

conducted using the same data set used to assess thestructural model. While Dunn et al. (1994) acknowledge that

collection of a second data set to test the structural model ispreferred, they acknowledge that the expense of collecting the

second data set is often prohibitive. This is the case here.Unidimensionality of the supply chain management

strategy, logistics performance, and financial performance




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scales was assessed using confirmatory factor analysis, as

recommended by Gerbing and Anderson (1988). It was

necessary to re-specify the supply chain management strategy

and logistics performance scales to achieve sufficient

unidimensionality. The supply chain management strategy

scale was reduced to six items and the logistics performance

scale to five items. Generally, items with standardized

coefficients less than 0.70 and items that contributed to

standardized residuals with values greater than 3.00 or less

than 23.00 were deleted (Raykov and Marcoulides, 2000).

The supply chain management strategy and logistics

performance scales, as re-specified, and the financial

performance scale yielded goodness-of-fit index (GFI)

values greater than 0.90 (Ahire et al., 1996), non-normed-fit

index (NNFI) and comparative-fit index values greater than

0.90 (Garver and Mentzer, 1999), and root mean square

error of approximation (RMSEA) values between 0.05 and

0.08 (Garver and Mentzer, 1999), indicating sufficient

unidimensionality. Because the marketing performance scale

includes only three items, it could not be subjected to a full

confirmatory factor analysis. It did, however, exceed the

requirements that all parameter estimates be of the proper

sign, significant, and greater than 0.70 as recommended by

Garver and Mentzer (1999). The scale items used in the

analysis to follow are identified in Table I.Alpha and construct-reliability values greater than or equal

to 0.70 and a variance-extracted measure of 0.50 or greater

indicate sufficient scale or factor reliability (Garver and

Mentzer, 1999). The alpha, construct-reliability, and

variance-extracted values for each of the scales exceeded the

recommended values indicating sufficient reliability.Convergent validity for the supply chain management

strategy, logistics performance, and financial performance was

assessed using the normed-fit index coefficient as

Figure 2 Logistics performance measurement model with standardized correlation coefficients (relative x2 ¼ 2:02, GFI ¼ 0:83, CFI ¼ 0:94,RMSEA ¼ 0:08)




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recommended by Ahire et al. (1996), with values greater than

0.9 indicating strong validity. The NFI for each of the scales

exceeds the 0.90 level, indicating sufficient convergent

validity. When the NFI is unavailable, as for the marketing

performance scale, Garver and Mentzer (1999) recommend

reviewing the magnitude of the parameter estimates for the

individual measurement items to assess convergent validity

with statistical significance of an estimate indicating a weak

condition of validity and an estimate greater than 0.7

indicating a strong condition. The parameter estimates for

the marketing performance items exceeded the criteria. All

scales exhibit convergent validity.Discriminant validity was assessed using a x2 difference test

for each pair of scales under consideration, with a statistically

significant difference in x2 indicating validity (Garver and

Mentzer, 1999; Ahire et al., 1996; Gerbing and Anderson,

1988). All possible pairs of the study scales were subjected to

x2 difference tests with each pairing producing a statistically

significant difference, indicating sufficient discriminant

validity. Predictive validity was assessed by determining

whether the scales of interest correlate as expected with

other measures (Ahire et al., 1996; Garver and Mentzer,

1999). A review of the correlation matrix (Table II) for the

study values supports claims of predictive validity for each

study variable. The study variables are positively correlated

with the coefficients significant at the 0.01 level.

A structural assessment of the full measurement model

indicates that the measurement model fits the data moderately

well with a relative chi-square (x2/degrees of freedom) of 2.02,

a RMSEA of 0.08, a GFI of 0.83, an NFI of 0.91, and a CFI

of 0.94. The full measurement model is displayed in Figure 2.

The individual measurement scales are considered sufficiently

unidimensional, reliable and valid and the fit of the

Table I Measurement scales

Supply chain management strategyPlease indicate the importance of each of the following issues/concerns to your organization’s supply chain management efforts (15 low

importance, 75 high importance)

SCMS3 Searching for new ways to integrate SCM activities

SCMS4 Creating a greater level of trust throughout the supply chain

SCMS6 Establishing more frequent contact with supply chain members

SCMS9 Communicating customers’ future strategic needs throughout the supply chain

SCMS10 Extending supply chains beyond your firm’s customers/suppliers

SCMS11 Communicating your firm’s future strategic needs to suppliers

Logistics performancePlease rate your company’s performance in each of the following areas as compared to the performance of your competitors (15much worse

than competition, 75much better than competition)

LOGPERF3 Delivery speed

LOGPERF5 Delivery dependability

LOGPERF6 Responsiveness

LOGPERF8 Delivery flexibility

LOGPERF10 Order fill capacity

Financial performancePlease rate your organization’s performance in each of the following areas as compared to the industry average (15 well below industry

average, 75 well above industry average)

FINPERF1 Average return on investment over the past three years

FINPERF2 Average profit over the past three years

FINPERF3 Profit growth over the past three years

FINPERF4 Average return on sales over the past three years

Marketing performancePlease rate your organization’s performance in each of the following areas as compared to the industry average (15 well below industry

average, 75 well above industry average)

MRKPERF1 Average market share growth over the past three years

MRKPERF2 Average sales volume growth over the past three years

MRKPERF3 Average sales (in dollars) growth over the past three years

Table II Descriptive statistics and correlations

Mean SD

A. Descriptive statistics (n5 142)

Supply chain management strategy

(SCMS) 4.91 1.18

Logistics performance (LP) 5.42 .93

Financial performance (FP) 4.63 1.22

Marketing performance (MP) 4.55 1.30

B. Correlation matrix (n5 142) SCMS LP FP MPSCMS 1.000

LP 0.230 * *1.000

FP 0.193 * *0.243 * *1.000

MP 0.248 * *0.225 * *0.706 * *1.000

Note: * *Correlation is significant at the 0.01 level (two-tailed)




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measurement model is considered sufficient to supportfurther assessment of the structural model.

Structural equation modeling results

Summary values for the study variables were computed byaveraging across the items in the scales. Descriptive statisticsand the correlation matrix for the summary variables arepresented in Table II. All correlation coefficients are positiveand significant at the 0.01 level.

Figure 3 illustrates the model with the structural equationmodeling results specified in the LISREL 8.8 output. Therelative x2 (x2/degrees of freedom) value of 2.02 is less thanthe 3.00 maximum recommended by Kline (1998). The rootmean square error of approximation (0.08) equals therecommended maximum of 0.08 (Schumacker and Lomax,2004). While NNFI (0.93) is above the recommended 0.90level (Byrne, 1998), the GFI (0.83) is not. These indices,however, are more heavily impacted by a relatively smallsample size, and, as Byrne (1998) points out, the comparativefit index (CFI) and incremental fit index (IFI) are moreappropriate when the sample size is small. The CFI (0.94)and IFI (0.94) both exceed the recommended 0.90 level(Byrne, 1998).

Four of the study hypotheses are supported by thestandardized estimates and associated t-values. Therelationship between SCMS and logistics performance (H1)is significant at the 0.05 level with an estimate of 0.23 and t-value of 2.52. The estimate of 0.21 for the relationshipbetween supply chain management strategy and marketingperformance (H2) is significant at the 0.05 level with a t-valueof 2.34. The relationship between supply chain managementstrategy and financial performance (H3) is not significant withan estimate of 0.00 and t-value of .04. The relationshipbetween logistics performance and marketing performance(H4) is significant at the 0.05 level with a standardizedestimate of 0.18 and an associated t-value of 2.02. Therelationship between logistics performance and financialperformance (H5) is not significant with a standardizedestimate of 0.09 and t-value of 1.35. The relationship betweenmarketing performance and financial performance issignificant at the 0.01 level with a standardized estimate of0.69 and a t-value of 7.67.

Generally, the results support the proposition that the

adoption of a supply chain management strategy leads to

improved supply chain performance, as measured by logistics

performance, which in turn leads to improved organizational

performance. It is very difficult to measure overall supply

chain performance directly. The logistics function, however, is

an externally focused supply chain function that has global, as

well as local, implications for managers in the manufacturing

sector.While the performance of manufacturing managers

continues to be evaluated based on organization-level

metrics related to the sales, market share, and profitability

of the organization, the results of this study support the

contention that manufacturing managers make decisions that

directly support supply chain performance which will, in turn,

enhance organizational performance. This expectation that

local managers first be concerned with and make decisions

that strengthen the supply chain is relatively new and may be

difficult for local managers to embrace. In this supply chain

era, however, success of the organization depends upon the

success of the supply chain or chains in which the

organization operates as a partner. These results support the

propositions that organizations now compete globally at the

supply chain level, that organizational performance depends

directly on supply chain performance, and that local

manufacturing managers focus on and make decisions that

enhance supply chain performance. In short, local

optimization now depends on global optimization. This is a

relatively new mindset but, as the results indicate, an

important one for manufacturing managers.

Conclusions

The theorized logistics performance model fits the data

moderately well providing support for four of the six study

hypotheses. As the focal construct, logistics performance is

positively impacted by supply chain management strategy and

directly impacts marketing performance which, in turn,

impacts financial performance. These results support the

positive relationship between logistics performance and

organizational performance within the manufacturing sector.

Figure 3 Theorized logistics performance structural model with standardized coefficients. t-Values are shown in parentheses (relative x2 ¼ 2:02,GFI ¼ 0:83, CFI ¼ 0:94, RMSEA ¼ 0:08)




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The success of the individual supply chain partners may

now depends upon the overall success of the supply chain(s)

in which the partners participate. Manufacturing managers

should now consider the implications for the overall supply

chain when making decisions related to their organization’s

manufacturing, purchasing, selling, and logistics processes.

Those processes are integrated and coordinated throughout

the supply chain to better serve the ultimate customers. It has

become critically important to measure the performance at

the supply chain level as well as organizational performance.

The theoretical proposition is that success at the supply chain

level will result in success at the organizational level. The

problem for both practitioners and researchers is that supply

chain performance is relatively difficult to measure. This

study incorporates an established measure of logistics

performance as a surrogate for supply chain performance.

Logistics is clearly a supply chain function in that it links

manufacturers and customers although those customers may

not be the ultimate customers in the supply chain.The results of this study support the broad contention that

manufacturers should focus on strengthening the supply

chain(s) in which they operate. Successful adoption of a

supply chain management strategy requires a supply chain

focus and efforts by managers to strengthen linkages with

both suppliers and customers. These stronger relationships

result in improved performance of supply chain related

functions such as logistics, purchasing and selling. In this

particular case, a supply chain focus resulted in improved

logistics performance, which in turn led to improved

organizational performance. While organizational managers

will likely still be evaluated on organization-level performance

metrics, the route to enhancing organizational performance

may well be through supply chain performance in the future.

In short, global optimization trumps local optimization.While the objectives of the study were successfully

accomplished, limitations of the study should be noted. A

survey methodology was used that resulted in a relatively low

response rate, raising concerns of potential non-response bias.

Although the two waves of responses were compared and no

evidence of bias was noted, a more direct assessment of the

potential bias utilizing data from a third wave and an intensive

follow-up on non-respondents would have strengthened the

study. Because responses related to both the dependent and

independent variables were collected from the same

individual, the potential for common method bias was a

concern. While subsequent testing for the bias relieved the

concern, collection of the strategy and performance data from

separate sources would also have strengthened the study.The study results have important implications from

manufacturing managers. The sustained, long-term success

of a manufacturing organization now depends upon

developing competitive advantage as a member of one or

more supply chains. While manufacturing managers have

embraced supply chain management as a strategic initiative,

they continue to search for appropriate tactical approaches to

implement the strategy. The logistics processes linking

manufacturer and customers play an important role in

supporting a supply chain management strategy. As

manufactures work to improve the logistics processes, they

support their organization’s supply chain strategy resulting in

improved performance for the overall supply chain and

ultimately their manufacturing organizations.

References

Ahire, S.L., Golhar, D.Y. and Waller, M.A. (1996),

“Development and validation of TQM implementation

constructs”, Decision Sciences, Vol. 27 No. 1, pp. 23-56.Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994),

“Customer satisfaction, market share, and profitability:

findings from Sweden”, Journal of Marketing, Vol. 58 No. 3,

pp. 53-66.Armstrong, J.S. and Overton, T.S. (1977), “Estimating

nonresponse bias in mail surveys”, Journal of Marketing

Research, Vol. 14 No. 3, pp. 396-402.Armistead, C.G. and Mapes, J. (1993), “The impact of

supply chain integration on operating performance”,

Logistics Information Management, Vol. 6 No. 4, pp. 9-14.Bowersox, D.J., Closs, D.J., Stank, T.P. and Keller, S.B.

(2000), “How supply chain competency leads to business

success”, Supply Chain Management Review, Vol. 4 No. 4,

pp. 70-8.Byrne, B.M. (1998), Structural Equation Modeling with

LISREL, PRELIS, and SIMPLIS, Lawrence Erlbaum

Associates, Mahwah, NJ.Carter, J.R. and Narasimhan, R. (1996), “Purchasing and

supply management: future directions and trends”,

International Journal of Purchasing & Materials

Management, Vol. 32 No. 4, pp. 2-12.Chen, I.J. and Paulraj, A. (2004), “Towards a theory of

supply chain management: the constructs and

measurements”, Journal of Operations Management, Vol. 22

No. 2, pp. 119-50.Chopra, S. and Meindl, P. (2004), Supply Chain Management:

Strategy, Planning, and Operation, 2nd ed., Pearson

Prentice-Hall, Upper Saddle River, NJ.Claycomb, C., Germain, R. and Droge, C. (1999), “Total

system JIT outcomes: inventory, organization and financial

effects”, International Journal of Physical Distribution and

Logistics, Vol. 29 No. 10, pp. 612-30.Cohen, S. and Roussel, J. (2005), Strategic Supply Chain

Management: The Five Disciplines for Top Performance,

McGraw-Hill, New York, NY.Cooper, M.C., Lambert, D.M. and Pagh, J.D. (1997),

“Supply chain management: more than a name for

logistics”, The International Journal of Logistics

Management, Vol. 8 No. 1, pp. 1-14.Council of Supply Chain Management Professionals (2007),

“Supply chain management and logistics management

definitions”, available at: www.cscmp.org/Website/

AboutCSCMP/Definitions/Definitions.asp.de Kluyver, C.A. and Pearce, J.A. II (2006), Strategy: A View

from the Top, 2nd ed., Pearson Prentice-Hall, Upper Saddle

River, NJ.Dunn, S.C., Seaker, R.F. and Waller, M.A. (1994), “Latent

variables in business logistics research: scale development

and validation”, Journal of Business Logistics, Vol. 15 No. 2,

pp. 145-72.Gammelgaard, B. and Larson, P.D. (2001), “Logistics skills

and competencies for supply chain management”, Journal

of Business Logistics, Vol. 22 No. 2, pp. 27-50.Garver, M.S. and Mentzer, J.T. (1999), “Logistics research

methods: employing structural equation modeling to test

for construct validity”, Journal of Business Logistics, Vol. 20

No. 1, pp. 33-57.




Volume 13 · Number 4 · 2008 · 317–327

325

Gerbing, D.W. and Anderson, J.C. (1988), “An updated

paradigm for scale development incorporating

unidimensionality and its assessment”, Journal of

Marketing Research, Vol. 25 No. 2, pp. 186-92.Green, K.W. Jr and Inman, R.A. (2005), “Using a just-in-

time selling strategy to strengthen supply chain linkages”,

International Journal of Production Research, Vol. 43 No. 16,

pp. 3437-53.Green, K.W. Jr, McGaughey, R. and Casey, K.M. (2006),

“Does supply chain management strategy mediate the

association between market orientation and organizational

performance?”, Supply Chain Management: An International

Journal, Vol. 11 No. 5, pp. 407-14.Green, K.W. Jr, Medlin, B. and Whitten, D. (2004),

“Developing optimism to improve performance: an

approach for the manufacturing sector”, Industrial

Management & Data Systems, Vol. 104 No. 2, pp. 106-14.Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and

Tatham, R.L. (2006), Multivariate Data Analysis, 6th ed.,

Pearson Prentice-Hall, Upper Saddle River, NJ.Harmon, H.A., Brown, G., Widing, R.E. II and Hammond,

K.L. (2002), “Exploring the sales manager’s feedback to a

failed sales effort”, Journal of Business & Industrial

Marketing, Vol. 17 No. 1, pp. 43-55.Ho, D.C.K., Au, K.F. and Newton, E. (2002), “Empirical

research on supply chain management: a critical review and

recommendations”, International Journal of Production

Research, Vol. 40 No. 17, pp. 4415-30.Klassen, R.D. and Jacobs, J. (2001), “Experimental

comparison of web, electronic and mail survey

technologies in operations management”, Journal of

Operations Management, Vol. 19, pp. 713-28.Kline, R.B. (1998), Principles and Practice of Structural

Equation Modeling, Guilford Press, New York, NY.Lai, K. and Cheng, T.C.E. (2003), “Supply chain

performance in transport logistics: an assessment by

service providers”, International Journal of Logistics:

Research and Applications, Vol. 6 No. 3, pp. 152-64.Lambert, D.M., Cooper, M.C. and Pagh, J.D. (1998),

“Supply chain management: implementation issues and

research opportunities”, The International Journal of Logistics

Management, Vol. 9 No. 2, pp. 1-19.Lambert, D.M. and Harrington, T.C. (1990), “Measuring

nonresponse bias in customer service mail surveys”, Journal

of Business Logistics, Vol. 11 No. 2, pp. 5-25.Larson, P.D. (2005), “A note on mail surveys and response

rates in logistics research”, Journal of Business Logistics,

Vol. 26 No. 2, pp. 211-22.Larson, P.D. and Halldorsson, A. (2004), “Logistics versus

supply chain management: an international survey”,

International Journal of Logistics: Research and Applications,

Vol. 7 No. 1, pp. 17-31.Lin, C. (2006), “Influencing factors on the innovation in

logistics technologies for logistics service providers in

Taiwan”, The Journal of American Academy of Business,

Vol. 9 No. 2, pp. 257-63.Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W.,

Smith, C.D. and Zacharia, Z.G. (2001), “Defining supply

chain management”, Journal of Business Logistics, Vol. 22

No. 2, pp. 1-25.Meredith, J.R. and Shafer, S.M. (2002), Operations

Management for MBAs, 2nd ed., Wiley, New York, NY.

Morash, E.A. and Clinton, S.R. (1997), “The role of

transportation capabilities in international supply chain

management”, Transportation Journal, Vol. 36 No. 3,

pp. 5-17.Morash, E.A. and Clinton, S.R. (1998), “Supply chain

integration: customer value through collaborative closeness

versus operational excellence”, Journal of Marketing: Theory

and Practice, Vol. 6 No. 4, pp. 104-20.Nahm, A.Y., Vonderembse, M.A. and Koufteros, X.A.

(2004), “The impact of organizational culture on time-

based manufacturing and performance”, Decision Sciences,

Vol. 35 No. 4, pp. 579-607.Novack, R.A., Rinehart, L.M. and Wells, M.V. (1992),

“Rethinking concept foundations in logistics management”,

Journal of Business Logistics, Vol. 13 No. 2, pp. 233-67.Oliver, N. and Delbridge, R. (2002), “The characteristics of

high performing supply chains”, International Journal of

Technology Management, Vol. 23 Nos 1-3, pp. 60-73.Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports in

organizational research: problems and prospects”, Journal

of Management, Vol. 12 No. 4, pp. 531-44.Rabinovich, E. and Knemeyer, A.M. (2006), “Logistics

service providers in Internet supply chains”, California

Management Review, Vol. 48 No. 4, pp. 84-108.Raykov, T. and Marcoulides, G.A. (2000), A First Course in

Structural Equation Modeling, Lawrence Erlbaum

Associates, Mahwah, NJ.Rodrigues, A.M., Bowersox, D.J. and Calantone, R.J. (2005),

“Estimation of global and national logistics expenditures:

2002 data update”, Journal of Business Logistics, Vol. 26

No. 2, pp. 1-15.Schramm-Klein, H. and Morschett, D. (2006), “The

relationship between marketing performance, logistics

performance and company performance for retail

companies”, International Review of Retail, Distribution and

Consumer Research, Vol. 16 No. 2, pp. 277-96.Schumacker, R.E. and Lomax, R.G. (2004), A Beginner’s

Guide to Structural Equation Modeling, Lawrence Erlbaum

Associates, Mahwah, NJ.Shao, X. and Ji, J. (2006), “Reconfiguration of

pharmaceutical logistics operations in China: an empirical

study”, Transportation Journal, Vol. 45 No. 4, pp. 52-66.Sheen, G. and Tai, C. (2006), “A study on decision factors

and third party selection criterion of logistics outsourcing:

an exploratory study of direct selling industry”, The Journal

of American Academy of Business, Cambridge, Vol. 9 No. 2,

pp. 331-7.Srivastava, S.K. (2006), “Logistics and supply chain practices

in India”, VISION – The Journal of Business Perspective,

Vol. 10 No. 3, pp. 69-79.Stank, T.P., Davis, B.R. and Fugate, B.S. (2005), “A strategic

framework for supply chain oriented logistics”, Journal of

Business Logistics, Vol. 26 No. 2, pp. 27-45.Stank, T.P., Keller, S.B. and Closs, D.J. (2002),

“Performance benefits of supply chain logistical

integration”, Transportation Journal, Vol. 41 Nos 2/3,

pp. 32-46.Tan, K.C. (2002), “Supply chain management: practices,

concerns, and performance issues”, Journal of Supply Chain

Management, Vol. 38 No. 1, pp. 42-53.Tan, K.C., Lyman, S.B. and Wisner, J.D. (2002), “Supply

chain management: a strategic perspective”, International




Volume 13 · Number 4 · 2008 · 317–327

326

Journal of Operations and Production Management, Vol. 22No. 6, pp. 614-31.

Vaidyanathan, G. (2005), “A framework for evaluating third-party logistics”, Communications of the ACM, Vol. 48 No. 1,pp. 89-94.

Vickery, S., Clanatone, R. and Droge, C. (1999), “Supplychain flexibility: an empirical study”, Journal of SupplyChain Management: A Global View of Purchasing and Supply,Vol. 35 No. 3, pp. 16-23.

Vokurka, R.J. and Lummus, R.R. (2000), “The role of just-in-time in supply chain management”, The InternationalJournal of Logistics Management, Vol. 11 No. 1, pp. 89-98.

Wisner, J.D. (2003), “A structural equation model of supply

chain management strategies and firm performance”,

Journal of Business Logistics, Vol. 24 No. 1, pp. 1-26.

Corresponding author

Dwayne Whitten can be contacted at: contacted at: dwhitten

@mays.tamu.edu




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