logistics performance efficiency, effectiveness, and differentiation.pdf

JOURNAL OF BUSINESS LOGISTICS, Vol. 31, No. 1, 2010 43

LOGISTICS PERFORMANCE: EFFICIENCY, EFFECTIVENESS, AND

DIFFERENTIATION

by

Brian S. Fugate

Colorado State University

John T. Mentzer

University of Tennessee

and

INTRODUCTION

The logistics function has long been under pressure to demonstrate its contribution to organizational performance (Rutner and Langley 2000). Consequently, research in logistics has examined the influence on organizational performance of high-performance logistics practices and capabilities. For instance, previous research has shown that excellence in performing logistics activities and capabilities is associated with superior organizational performance (Lambert and Burduroglo 2000; Lynch, Keller, and Ozment 2000). Despite this evidence, doubt remains concerning the strength of the direct link between logistics performance and organizational performance. Further investigation is needed, therefore, to understand logistics performance and to reinforce the potential value of logistics within the organization.

In attempting to drive performance improvements, managers often struggle with multiple, seemingly

conflicting, objectives (Steers 1975). Logistics managers have traditionally assumed they face a tough choice: either strive for efficiency; or strive for effectiveness. Though recent logistics research has suggested that these two performance objectives are mutually exclusive (Griffis et al. 2004), it is possible that this dilemma is unwarranted. For example, it would be difficult to rank Dell’s performance in the late 1990’s as either highly efficient or effective, but more appropriately highly efficient and effective. Further, logistics managers must strive for more than efficiency and effectiveness to ensure that they are providing the best comparative (i.e., differentiated) net value to customers in order to compete in today’s hypercompetitive marketplace. More demanding customers, short product life cycles, rapid technological changes, globalization, and the need to deliver on Wall Street’s ever-rising expectations (Singhal and Hendricks 2002), may in fact demand break-through thinking to simultaneously develop highly efficient, effective, and differentiated logistics activities.

A key goal of this research was to model logistics performance with the concept of simultaneous pursuit of

efficiency, effectiveness, and differentiation in mind. Past research portrayed logistics performance as a first order construct manifested—or “reflected”—by performance indicators (Helm 2005). In breaking with past research, and consistent with a portrayal of logistics performance as consisting of dimensions of efficiency, effectiveness, and differentiation, we use a formative, second-order construct for logistics performance. To our knowledge, this is the first formative construct applied in logistics research. A better understanding of how to accurately specify constructs as reflective or formative is important to the logistics discipline because logistics research is increasingly using complex latent variables to test behavioral logistics phenomena (Dunn, Seaker, and Waller 1994), especially

44 FUGATE, MENTZER & STANK

since research has shown misspecification can greatly compromise the validity of the findings (Jarvis, Mackenzie, and Podsakoff 2003).

This research makes several important contributions toward the objective of enhancing our understanding of top

performing logistics functions and documenting their influence on organizational performance. First, it draws from research in organizational and strategic management and logistics to develop theoretically based conceptualizations of logistics efficiency, effectiveness, and differentiation, and provides empirical results contradicting the traditionally assumed mutually exclusive relationship among them. Further, it introduces the logistics discipline to formative constructs and presents criteria for determining whether a construct is formative or reflective. Finally, this research empirically investigates the influence of the performance of the logistics function on organizational performance. Importantly, our perceptual measures for organization performance were strongly correlated with secondary, objective financial data collected on participating firms from Compustat.

MODEL DEVELOPMENT

With the increasing awareness of the strategic implications of logistics (Cheng and Grimm 2006; Stank, Davis, and Fugate 2005) and the growing awareness of the benefits of leveraging logistics to increase customer value (Mentzer and Williams 2001; Stank et al. 2003), measuring the performance of logistics has become a high priority (Griffis et al. 2007). Understanding logistics performance has long been of interest to logistics researchers and has been conceptualized and empirically tested in a variety of ways (for an expansive list of logistics metrics, see Enslow et al. 2005).

Traditional logistics performance measures include “hard” measures such as service (e.g., order cycle time and

fill rates), cost, and return on assets or investment (Brewer and Speh 2000; Morash, Dröge, and Vickery 1996) and soft measures, such as managers’ perceptions of customer satisfaction and loyalty (Chow, Heaver, and Henriksson 1994; Holmberg 2000). More recently, some have maintained that logistics performance measures be linked to corporate strategy (Lambert and Pohlen 2001; Zacharia and Mentzer 2004) and more explicitly incorporate customers’ perspectives (Brewer and Speh 2000; Mentzer, Flint, and Kent 1999).

Mentzer and Konrad (1991) defined logistics performance as effectiveness and efficiency in performing

logistics activities. Langley and Holcomb (1992) extended this definition by adding logistics differentiation as a key element of logistics performance because the value customers receive from logistics activities also serves as an indicator of logistics performance. They contended that logistics could create value through efficiency, effectiveness, and differentiation. For instance, value can be created through customer service elements such as product availability, timeliness and consistency of delivery, and ease of placing orders. If logistics can create value through the inimitability of its logistics activities, a firm may be able to differentiate itself from its competitors. Excellence in logistics performance requires superiority when compared to competitors (i.e., differentiation). Later, Smith (2000) extended Langley and Holcomb (1992) to define logistics performance as a second-order construct consisting of logistics efficiency, effectiveness, and differentiation. Bobbitt (2004) extended Smith (2000) to refine some of the measures. In summary, virtually all of the diverse logistics performance criteria presented in previous literature can be subsumed under the dimensions of effectiveness, efficiency, and differentiation, as shown in Figure 1. Therefore, the cumulative evidence of previous research suggests that logistics performance is multi-dimensional

and is defined as the degree of efficiency, effectiveness, and differentiation associated with the accomplishment of

logistics activities (Bobbitt 2004; Cameron 1986).

Based on research long-rooted in the management discipline, effectiveness is defined as the resource getting

ability, and refers to an absolute level of outcome attainment (Ostroff and Schmitt 1993). It has been defined as the ratio between the real or actual outputs and normal or expected outputs (Katz and Kahn 1978; Sink 1985). In logistics, it has been described as the ability to achieve pre-defined objectives, for example, in meeting customer requirements in critical result areas (e.g., product guarantee, in-stock availability, fulfillment time, convenience) (Langley and Holcomb 1992). Similarly, we adopt Mentzer and Konrad’s (1991) definition of logistics effectiveness as the extent to which the logistics function’s goals are accomplished.


FIGURE 1

A MODEL OF LOGISTICS PERFORMANCE

Efficiency refers to the internal functioning of logistics and generally is considered best represented through

some ratio of the normal level of inputs to the real level of outputs (Chamberlain 1968; Van der Meulen and Spijkerman 1985). Specifically, it is the ratio of resources utilized against the results derived (Mentzer and Konrad 1991). It is considered the ability to provide the desired product/service mix at a level of cost that is acceptable to the customer (Langley and Holcomb 1992). In a broader sense, it is the ability of the logistics function to manage resources wisely. Thus, we adopt the definition of efficiency as the measure of how well the resources expended are

utilized.

Logistics research has generally assumed that efficiency and effectiveness are mutually exclusive. This conceptualization of logistics performance has led to the “either-or” debate, where logistics research implies that managers should strive for either effectiveness or efficiency. This perspective maintains that “pursuing one or the other to its extreme precludes pursuit of the other” (Griffis et al. 2004, p. 100). This view implies that logistics performance that progresses along one dimension entails regression along another.

As an example of this “either-or” argument, Fisher’s (1997) popular theory contends that supply chains should either be designed for efficiency by emphasizing physical functioning in delivering goods, or for responsiveness by asserting the market mediating function for conveying information. In contrast, recent empirical research (Selldin and Olhager 2007) finds that firms that select properties from efficient and responsive supply chains achieve higher financial performance than their competitors that select properties from one or the other. This sheds light on the fact that firms may not consider performance dimensions (e.g., efficiency and effectiveness) to be inversely related, but rather they may pursue both concurrently.

In fact, since Barnard’s (1939) contention that effectiveness and efficiency are two central organizational goals,

a number of organizational and strategic management researchers (Katz and Kahn 1978; Kotabe 1988; Miller 1981; Steers 1975; Venkatraman and Ramanujam 1986) have investigated this debate. The controversy centers on the possible trade-offs between the dimensions underlying the performance construct (Davis and Pett 2002). Mahoney (1988) argued that trade-offs exist between efficiency and effectiveness and, thus, result in organizations being either efficient or effective, but not both. Others, however, maintain that organizations are complex and simultaneously pursue multiple goals (Steers 1975). For example, Ford and Schellenberg (1982) contend the highest performing organizations emphasize both efficiency and effectiveness. Further, Ostroff and Schmitt’s (1993, p. 1345) empirical research findings imply that organizations can be highly “effective, efficient, both, or neither.”

Beyond efficiency and effectiveness, logistics activities must also provide the best comparative net value to

customers (Stahl and Bounds 1991) in order to compete in today’s competitive marketplace. Because of the

FIGURE 1

A MODEL OF LOGISTICS PERFORMANCE


centrality of logistics to customer value creation (Flint et al. 2005; Lambert, García-Dastugue, and Croxton 2005), the performance of logistics activities must be perceived as differentially superior to competitors in the same market segment(s) (Williamson, Spitzer, and Bloomberg 1990).

The approach of comparing performance to competitors has long been rooted in strategic management research

(Wernerfelt 1984). For example, Hunt and Morgan (1995) apply the resource-based view of the firm to suggest that competitive advantage results from the firm, relative to its competitors, being able to produce an offering for some market segment that is perceived to be of superior value. Quality management research and practices also emphasize the critical role of the relative performance evaluation process of benchmarking (Basak, Shapiro, and Teplá 2006). The familiar phrases “best-in-class” and “world-class” and the popularity of benchmarking services, point to organizations’ desires to search for excellence. In the logistics discipline, Langley and Holcomb (1992) term this dimension of comparing results of logistics activities to competitors as “logistics differentiation.” Evidence collectively reveals that the logistics function as a whole should strive to minimize the ratio of resources utilized against derived results (efficiency), accomplish pre-defined objectives (effectiveness), and gain superiority when compared to competitors (differentiation) (Bobbitt 2004). Extending previous research (Ostroff and Schmitt 1993), the following hypotheses are offered:

H1: Logistics effectiveness and logistics efficiency are positively related.

H2: Logistics effectiveness and logistics differentiation are positively related.

H3: Logistics differentiation and logistics efficiency are positively related. Logistics Performance as a Second-Order Formative Construct

As depicted in Figure 1, and consistent with the theory that logistics performance is composed of three underlying dimensions (logistics effectiveness, efficiency, and differentiation), logistics performance is conceptualized and modeled as a formative second-order construct. Formative constructs (also known as composite latent variables) differ from reflective constructs (also known as principle factor latent variables), and specifying a model as one or the other is based primarily on theoretical considerations (Chin and Todd 1995). Several articles (Diamantopoulos and Siguaw 2006; Diamantopoulos and Winklhofer 2001; Jarvis, MacKenzie, and Podsakoff 2003) provide rules for determining whether a construct should be modeled as formative or reflective, and are summarized in Table 1.

Many researchers assume the relationships between constructs and their indicators or dimensions in research

models are reflective, and a majority of constructs that have been incorrectly modeled were formative constructs incorrectly analyzed as reflective constructs (Diamantopoulos and Siguaw 2006; Helm 2005). This is not a trivial decision—Jarvis, MacKenzie, and Podsakoff’s (2003) simulation results indicate that incorrectly specifying constructs as formative or reflective can inflate standardized estimates as high as 555 % or suppress them as much as 93 %. Model misspecification of even one formatively measured construct can have serious consequences for the conclusions drawn from that model.

According to the decision rules for determining whether a construct is formative or reflective, it is apparent that

the second-order logistics performance construct is formative, with the defining constructs of logistics effectiveness, efficiency, and differentiation. Changes in logistics effectiveness, efficiency, and differentiation (i.e., first-order dimensions) cause changes in logistics performance (i.e., the second-order latent construct), not visa versa. Also, dropping one of the first-order dimensions alters the conceptual domain of logistics performance. In addition, all the antecedents for logistics efficiency, effectiveness, and differentiation are not the same (i.e., the nomological net for the first-order dimensions differ). For example, some organizational cultures (one potential antecedent) may drive a low cost mentality, but have no influence on the logistics professional’s desire or ability to accomplish the overall logistics goals. Ostroff and Schmitt’s (1993) results support this contention that different configurations of organizational characteristics (i.e., antecedents) are differentially related to effectiveness and efficiency. Therefore, the second-order logistics performance construct is modeled formatively.

Applying Jarvis, Mackenzie, and Podsakoff’s (2003) classification of second-order factor models supports

specifying a reflective first-order, formative second-order model (see Jarvis, Mackenzie, and Podsakoff 2003, p. 204 for the classification table) where logistics performance is modeled using an “index” represented by the dimensions of logistics effectiveness, efficiency, and differentiation. Thus, changes in the underlying logistics effectiveness,


efficiency, and differentiation dimensions are hypothesized to cause variation in logistics performance, the second-order latent construct.

TABLE 1

CRITERIA FOR DETERMINING WHETHER A CONSTRUCT IS REFLECTIVE OR FORMATIVEa

Reflective Formative

Direction of causality is from construct to indicators or 1st-order dimensions

Direction of causality is from indicators or 1st-order dimensions to construct

Indicators or 1st-order dimensions are manifestations of the construct

Indicators or 1st-order dimensions are defining characteristics of the construct

Indicators or 1st-order dimensions should be interchangeable, have same or similar content, and share a common theme.

Indicators or 1st-order dimensions may, but do not necessarily need to be interchangeable, have same or similar content, or share a common theme.

Dropping an indicator or 1st-order dimension should not alter the conceptual domain of the construct

Dropping an indicator or 1st-order dimension should alter the conceptual domain of the construct

Indicators or 1st-order dimensions are expected to covary with each other

Indicators or 1st-order dimensions may covary positively, negatively, or be neutral with each other

Nomological net for the indicators or 1st-order dimensions should not differ. For example, antecedents for 1st-order dimensions may not differ.

Nomological net for the indicators or 1st-order dimensions may differ. For example, antecedents for 1st-order dimensions may differ.

aAdapted from Jarvis, MacKenzie, and Podsakoff (2003)

H4a-c: Logistics performance is a second-order formative construct composed of three dimensions:

(a) logistics effectiveness; (b) logistics efficiency; and (c) logistics differentiation.

As part of the broader goal of investigating the nomological validity (Lynch et al. 1983) of the logistics

performance construct (also called predictive validity; i.e., is the construct related to its theoretical consequences (Garver and Mentzer 1999), and its more strategic implications within the organization (Richey, Daugherty, and Roath 2007), we examine the influence of logistics performance on organizational performance. As logistics researchers and practitioners seek to highlight the importance of logistics to the organization (Novack, Rinehart, and Langley 1994; Zacharia and Mentzer 2004), understanding its impact on organizational performance is critical.

Previous research supports the direct link of logistics activities and competencies with organizational

performance (Claycomb, Germain, and Dröge 2000; Dröge and Germain 2000; Ellinger, Daugherty, and Keller 2000; Lynch, Keller, and Ozment 2000). Further, the strategic profit model, which illustrates the relationships among income statement and balance sheet line items (Davis 1950; Stapleton et al. 2002; Stephenson 1976), also reveals the influence of logistics performance on organizational performance (Lambert and Burduroglo 2000). Increases in logistics efficiency, effectiveness, and differentiation variables decreases expenses, inventory, and cash requirements (Larsen and Lusch 1990; Mentzer and Konrad 1991), and increases inventory availability, timely delivery, on-time and damage-free deliveries, line-item fill rates, and sales (Langley and Holcomb 1992; Mentzer and Williams 2001; Morash, Dröge, and Vickery 1996), which improve net margin and asset turnover, which improves return on assets and overall firm performance.

H5: High levels of logistics performance are positively associated with high levels of organizational

performance.


METHODOLOGY

We applied survey methodology and structural equation modeling analysis to address the theoretical, methodological, and managerial issues raised to test the hypotheses.

Data Collection

We collected data in a nationwide survey using a list obtained from the Council of Supply Chain Management Professionals (CSCMP). CSCMP members were pre-screened and pre-contacted to determine if they met the criteria for our target population. The targeted respondents were the firm’s mid- and top-level logistics professionals, because they are believed to have a higher degree of knowledge of virtually all the logistics areas within the organization and, thus, logistics and organizational performance. Respondents were limited to those within manufacturing firms.

One-hundred and five randomly selected managers from the 530 firms in the sampling frame were emailed links

to the web-based survey (Griffis, Goldsby, and Cooper 2003) to pretest, validate, and revise the adapted and newly developed measures (Dillman 2000). Eighty-four respondents completed the questionnaire (80 % response rate).

For the final survey, 336 (79 %) of the remaining 425 respondents completed the revised questionnaire. The

respondents had an average of 92 direct and indirect reports, averaged over seven years experience in their department, and held titles that fit our sample criteria. Average annual sales volume and industry of the pretest and final test respondents are in Appendix A. The data were estimated to be normal, since the farthest skewness or kurtosis from zero was -0.95. We analyzed non-response bias by collecting from 32 non-respondents responses for five substantive items related to key constructs and testing for significant differences (Mentzer and Flint 1997). There were no significant differences (p < 0.05) in responses to any item.

Development of the second-order formative logistics performance construct

We adhered to Diamantopoulos and Winklhofer’s (2001, p. 271) guidelines for developing the index (i.e., the first-order dimensions) for the second-order formative logistics performance construct. Successful index construction involves content specification, indicator specification, indicator collinearity, and external validity (Diamantopoulos and Winklhofer 2001). Content specification refers to appropriately defining the breadth of the domain of the content of the focal construct. Indicator specification refers to ensuring that the indicators chosen cover the entire scope of the formative construct as described under content specification. Support for content and indicator specification was provided in the theory and hypotheses development section of this manuscript. Specifically, the definition of logistics performance was developed by integrating previous research, and together the dimensions capture the facets (i.e., efficiency, effectiveness, and differentiation) of that definition, which provides support for reasonable comprehensiveness. Further, to address the potential issue of indicator collinearity (presence of multicollinearity), we calculated the variance inflation factors among the first-order dimensions. The maximum variance inflation factor was 1.282, which is far below the common cut-off threshold of 10. Thus, support exists that indicator collinearity is not a problem. Lastly, external validity refers to the challenge of assessing the suitability of indicators given the internal consistency perspective present in the nature of formative measurement. We addressed external validity concerns of formative constructs by including three additional reflective items to tap a general, overall level of logistics performance (see further explanation of these items in the following section).

Scale development of reflective items

We developed and adapted the 7-point Likert measurement scales following procedures of Churchill (1979) and Dunn, Seaker, and Waller (1994). We participated in an iterative process of reviewing, pilot testing, and revising the survey with a total of eight subject matter experts and nine logistics managers, which provided the basis of the pretest survey. This resulted in 29 items, plus four items for the Global Reach construct used to test common method bias.

While the second-order Logistics Performance construct is formative in that it is formed by an “index”

represented by the three first-order dimensions (efficiency, effectiveness, and differentiation), we followed


guidelines from Diamantopoulos and Winklhofer (2001) to ensure model identification and external validity by additionally including three newly created reflective items (Q1, Q2, and Q3 in Appendix B). As recommended, these items tap the overall level of logistics performance (see Figure 2 for a depiction of which relationships in the model are formative and which are reflective).

FIGURE 2

FORMATIVE AND REFLECTIVE RELATIONSHIPS

Logistics Efficiency, Logistics Effectiveness, and Logistics Differentiation measures were adapted from Bobbitt (2004) and Smith (2000). Organizational Performance measures were adopted from Matsuno, Mentzer, and Özsomer (2002). Subjective scales were used since it provided a larger available sample size than if objective measures were requested. Previous research demonstrates that managerial assessments are consistent with external secondary data (Kannan and Tan 2006; Tan et al. 1999; Venkatraman and Ramanujam 1986) and objective internal performance (Dess and Robinson 1984, 1987; Slater and Narver 1994). In addition, though the survey participants were assured anonymity, 44 respondents voluntarily provided their firm names, which were also available in the Compustat database. We compared four objective indicators obtained via Compustat (either directly or computed) for those 44 firms with the Likert-scale measures, which resulted in a positive, significant correlation (p < .01) of 0.69 for ROI,0.64 for ROA, 0.57 for return on sales, and 0.67 for sales growth.

Measurement Model

For the reflective items in the model, we used Amos 6.0 (Amos 2005) to purify the scales of the final survey results prior to hypotheses testing. Six items were removed for substantive and statistical reasons (Anderson and Gerbing 1988). The overall fit statistics for the final measurement model for the reflective items in the model demonstrated acceptable fit ( 2 of 539.86 and degrees of freedom [d.f.] of 242 = 2/d.f. = 2.27; comparative fit index [CFI] = 0.96; root mean square error of approximation [RMEA] = 0.06). The strong fit of the overall measurement model, the statistical significance of standardized estimates of the items on their respective constructs, and the appropriate direction of the standardized estimates demonstrate support for both unidimensionality and convergent validity (see Table 2). The final 29 acceptable items (see Appendix B) exhibited modification indices less than 10, standardized residuals less than 2, and standardized parameter estimates greater than 0.70, which demonstrate support for discriminant validity among the items. Coefficient alpha for each construct was 0.89 or greater and average variance extracted was 0.65 or greater, which demonstrates support for the final scale reliability. The standardized estimates and significance for each item and reliability scores for each construct are shown in Table 2.

Organizational Performance

Logistics Performance

Logistics Efficiency

Logistics Effectiveness

Logistics Differentiation

.51***

H5

H4c H4b

H4a

Q1

Q2

Q3

Q24

Q25

Q26

Q27

Q28

Q29

Q4 Q6 Q7 Q8 Q10 Q11 Q12 Q13 Q14 Q15 Q17 Q18 Q19 Q21 Q22

.29***

.18***

.59***

Formative relationship

Reflective relationship

Formative relationship

Legend:


TABLE 2

CONVERGENT VALIDITY AND RELIABILITY

Construct

Item

IDb

Standardized

Estimatec

Squared

Multiple

Correlations

Average

Variance

Extracted

Cronbach's

Alpha

Logistics Performance Q1 0.91 0.84 0.88 0.96 Logistics Performance Q2 0.95 0.90 Logistics Performance Q3 0.96 0.93

Logistics Differentiation Q4 0.86 0.74 0.66 0.92 Logistics Differentiation Q6 0.79 0.63 Logistics Differentiation Q7 0.78 0.61 Logistics Differentiation Q8 0.86 0.74 Logistics Differentiation Q10 0.78 0.61 Logistics Differentiation Q11 0.78 0.60

Logistics Efficiency Q12 0.86 0.74 0.78 0.95 Logistics Efficiency Q13 0.90 0.80 Logistics Efficiency Q14 0.92 0.84 Logistics Efficiency Q15 0.89 0.79 Logistics Efficiency Q17 0.86 0.73

Logistics Effectiveness Q18 0.81 0.66 0.69 0.89 Logistics Effectiveness Q19 0.73 0.53 Logistics Effectiveness Q21 0.89 0.79 Logistics Effectiveness Q22 0.87 0.76

Organizational Performance Q24 0.90 0.82 0.76 0.95 Organizational Performance Q25 0.93 0.76 Organizational Performance Q26 0.84 0.71 Organizational Performance Q27 0.84 0.70 Organizational Performance Q28 0.77 0.59 Organizational Performance Q29 0.94 0.87

bCorresponds to item number in Appendix B cp < 0.001

Discriminant validity was also tested by running a series of nested CFA model comparisons in which the covariance between each pair of constructs (one pair at a time) was constrained to one (Anderson and Gerbing 1988; Bagozzi and Yi 1988). The 2 difference tests for all 10 pairs of constructs were significant at p < 0.05. We also evaluated whether the intercorrelations among the constructs were less than 0.70, which suggests the constructs had less than half their variance in common (MacKenzie, Podsakoff, and Jarvis 2005). All pairs of constructs met this cut-off, except for the intercorrelation of 0.77 between Logistics Performance and Logistics Differentiation. This was expected since Logistics Differentiation is a first-order construct of the second-order construct, Logistics Performance, and thus does not compromise the discriminant validity criteria (Jarvis, MacKenzie, and Podsakoff 2003). Additionally, we followed Fornell and Larcker’s (1981a; 1981b) guidelines for assessing discriminant validity by comparing the average variance extracted for each construct with the square of the correlation between all possible pairs of constructs. The square of the correlation between all pairs of constructs was less than the average variance extracted in all cases. Thus, all three of our assessments offer support that the constructs exhibit discriminant validity (see Table 3).

We applied procedural methods to minimize the potential for common method bias since both independent and

dependent measures were obtained from the same source. There were no reverse-coded items, and all the hypotheses were stated in a positive direction (Swink and Song 2007). We conducted prequalification calls to ensure our respondents were mid- to senior-level managers and had high levels of relevant knowledge, which tends to mitigate single source biases (Mitchell 1994). We also reduced method biases by separating the predictor and criterion variable items over a lengthy survey instrument and by assuring the survey participants that their responses would be kept anonymous (see Podsakoff et al. 2003, for a review of procedures to reduce common method bias). In addition, we consulted previous research and conducted an iterative process of reviewing, pilot testing, and


revising the survey with a total of eight subject matter experts and nine logistics managers to develop new and adapt existing survey items, each of which minimizes the potential for context effects (Lindell and Whitney 2001).

TABLE 3

DISCRIMINANT VALIDITYd

Constructs

Logistics

Performance

Logistics

Differentiation

Logistics

Efficiency

Logistics

Effectiveness

Organizational

Performance

Logistics Performance 0.88 0.59 0.37 0.18 0.25 Logistics Differentiation 0.77 0.66 0.22 0.09 0.27 Logistics Efficiency 0.61 0.47 0.78 0.07 0.12 Logistics Effectiveness 0.43 0.30 0.27 0.69 0.06 Organizational Performance 0.50 0.52 0.35 0.25 0.76 dDiagonal entries are average variances extracted; entries below the diagonal are correlations; and entries above the diagonal represent the squared correlations.

In addition to minimizing the potential for common method bias, we tested for common method bias by

including a marker variable, a construct that theoretically should not be related to any of the other constructs in the model (Lindell and Whitney 2001; Menon, Bharadwaj, and Howell 1996). We adopted Fawcett, Calantone, and Smith’s (1996) Global Reach construct that measures the ability to better manage dispersed operations and is comprised of four reflective items (Q30-Q33 in Appendix B). The Global Reach scale met all the criteria for convergent validity, discriminant validity, and reliability. Coefficient alpha for the scale was 0.90 and all standardized estimates were above 0.70 for the construct. We allowed the five substantive constructs in Figure 1 to load onto one second-order factor and compared that model to one that also allowed the Global Reach construct to load onto the second-order factor to assess common method bias. The model with Global Reach resulted in worse fit and all paths were significant at p < 0.001, except the path to Global Reach (p = 0.20). Further, the high correlation of the Compustat data with the self-reported organizational performance variables provided additional support for the absence of common method bias. The cumulative evidence offers strong support that the research did not exhibit common method bias.

Structural Model

Structural equation modeling, using Amos 6.0 (Amos 2005), was applied to analyze the relationships in the model in Figure 1, since it is appropriate for complex, multivariate data and testing hypotheses regarding relationships among observed and latent variables (Hoyle 1995). In testing formative constructs, as within our model, there are two general causal modeling approaches: the covariance-based methods (e.g., LISREL); or the variance-based method, known as partial least squares (PLS). Covariance-based methods are more appropriate for confirming theory and parameter estimation, and require large samples sizes and normally distributed data (Fornell and Bookstein 1982; Hoyle 1995). PLS, in contrast, is more appropriate when theory is lacking regarding the nature of relationships among constructs, dimensions, and their indicators and for prediction purposes (Chin 1998; Haenlein 2004; Hulland 1999). While PLS is also often applied for analyzing formative constructs because it is less restricting, in that small sample sizes are acceptable and it has no data distribution requirements (Chin and Newsted 1999; Johnson, Herrmann, and Huber 2006). We applied the covariance-based method because our purpose is not theory generation, but rather confirming, and we have a large sample size and normally distributed data.


RESULTS

The results of the structural model provided acceptable overall fit statistics: 2 of 556.99 and degrees of freedom [d.f.] of 245 = 2/d.f. = 2.23; comparative fit index [CFI] = 0.96; root mean square error of approximation [RMEA] = 0.06. Contrary to the “either-or” contention, a positive correlation exists between logistics effectiveness and logistics efficiency, logistics effectiveness and logistics differentiation, and logistics differentiation and logistics efficiency (see Table 3). Thus, the results support H1, H2, and H3.

In addition, significant (p < 0.001) and positive relationships exist between logistics performance and logistics

effectiveness (standardized estimate = 0.18), logistics performance and logistics efficiency (standardized estimate = 0.29), and logistics performance and logistics differentiation (standardized estimate = 0.59). This, along with the strong fit statistics of the overall model, provides support that logistics performance is a second-order construct composed of the three dimensions: logistics efficiency; effectiveness; and differentiation (Jarvis, MacKenzie, and Podsakoff 2003). Specifying a construct as formative or reflective is based primarily on theoretical considerations (Chin and Todd 1995), but achieving strong model fit and comparing formative and reflective models can also provide empirical support for this specification.

Following guidelines for comparing models (Morgan and Hunt 1994; Rust, Lee, and Valente 1995), we

compared the overall fit of our model to a reflective model, based on degrees of freedom and the number of hypothesized parameters that are significant. While both model-implied covariance matrices fit the sample covariance matrix well, and all the hypothesized parameters were significant for both models, examining the 2 difference tests between these models reveal the overall fit of our proposed formative model is better than the reflective model (p < 0.001). Also, the 2 divided by degrees of freedom is lower and the parsimony fit indices are better for the formative model. Though the logistics performance dimensions were positively correlated, both reflective and formative dimensions may be correlated (Diamantopoulos and Winklhofer 2001). Thus, our results provided support for H4a-c that logistics performance is a second-order construct formed by logistics efficiency, effectiveness, and differentiation. Lastly, logistics performance has an expected significant positive relationship on organizational performance (standardized estimate = 0.51, p < 0.001), which provides support for H5.

CONCLUSIONS AND IMPLICATIONS

Our research contributes to a better understanding of logistics performance, the interrelationships among its dimensions, and its impact on overall organizational performance for manufacturing firms. These findings provide both theoretical and managerial insights. Our empirical research findings contradict the traditionally assumed “either-or” relationship between efficiency and effectiveness (Fisher 1997). Our results indicate that pursuing one does not preclude pursuit of the other, but rather the performance dimensions perhaps reinforce each other. Logistics professionals need not assume a trade-off exists between efficiency, effectiveness, and differentiation. Strategy formulation, and the resulting design of structures and processes, does not need to begin with choosing only one of the logistics performance dimensions. All three may be pursued simultaneously. This suggests managers should continue to find approaches to break-through these assumed trade-offs. They could pursue efficiency, effectiveness, and differentiation of their logistics activities concurrently, which would force managers to be innovative and develop strategies and tactics that overcome these trade-offs. The past success of Dell’s direct model and Zara’s formula for supply chain success are examples of innovative approaches that provide increased efficiency, effectiveness, and differentiation.

Our results also provide empirical support for operationalizing logistics performance as formed by logistics

efficiency, effectiveness, and differentiation. The developed measurement scales of the logistics performance construct, and its dimensions, satisfied the theoretical and statistical criteria for unidimensionality, internal consistency reliability, and construct validity. This provides an avenue for testing the performance impact of logistics phenomena of interest in future research. Future researchers can consider applying the second-order formative logistics performance construct as an outcome variable in their research. Interestingly, the relatively higher path weight (0.59) of logistics differentiation on logistics performance, when compared to the other two logistics performance dimensions, suggests understanding logistics activities in comparison to competitors is vitally important. This emphasizes the additional need for logistics managers to systematically search and monitor logistics results of other firms in their industry and compare their logistics activities with those of their competitors.


Additionally, our research emphasizes the importance of specifying constructs as formative or reflective. This is increasingly important as logistics research continues to include more complex behavioral variables (Dunn, Seaker, and Waller 1994) and uses first-order (Frankel, Naslund, and Bolumole 2005; Keller et al. 2002) and second-order latent variables (Golicic, Foggin, and Mentzer 2003; Min and Mentzer 2004). Researchers should carefully apply the criteria in Table 1 during construct development and when adopting existing constructs in future research, since misspecification of a construct as reflective or formative has been shown to significantly distort empirical results (Jarvis, MacKenzie, and Podsakoff 2003). Further, we present conditions to determine when researchers apply variance-based (e.g., PLS) versus covariance-based (e.g., LISREL) methods to analyze formative constructs. Future research would benefit from applying PLS in exploratory research where theory is lacking regarding the nature of relationships among constructs, dimensions, and their indicators, whether formative or reflective models are adopted.

Managers and academics have long been interested in and have investigated the relative power and value of

different functions within the firm (Enz 1988; Luo, Slotegraaf, and Pan 2006). Today’s supply chain and boundary spanning era (Kent and Flint 1997) have brought increased attention to understanding logistics’ influence and importance in the firm (Novack, Rinehart, and Langley 1994; Zacharia and Mentzer 2004). Though previous research has provided support for the impact of specific logistics activities and capabilities on organizational performance (Lambert and Burduroglo 2000; Lynch, Keller, and Ozment 2000), the standardized estimate of 0.51 of H4 empirically substantiates the magnitude of the direct link between logistics performance and organizational performance. According to our results, excellence in logistics is related to higher organizational performance.

We do not propose that the items in our logistics performance measurement scales are the only metrics that

logistics managers should apply. To the contrary, managers should identify those metrics most appropriate to their organizational and environmental context. Such metrics could be used to capture the performance of more detailed logistics activities as a means to achieving effectiveness, efficiency, and differentiation of the overall logistics function. Additionally, future research should explore the link between individual logistics professionals’ specific performance measurement criteria (Manrodt et al. 1999; Waller and Novack 1995) and the performance of the logistics function as a whole (i.e., Figure 1). For instance, future research could apply Griffis et al.’s (2004) model to determine the monitoring needs of specific individual logistics professionals and how their collective efforts impact overall performance of the logistics function. While some logistics managers may individually focus more on one performance monitoring criteria (e.g., operational or strategic, efficiency or responsiveness, process or functional), our results suggest the overall performance of the logistics function as a whole should produce high levels of logistics effectiveness, efficiency, and differentiation, to positively affect the performance of the organization.

These findings must be interpreted against the backdrop of the methodological limitations of our research,

which offer additional future research opportunities. First, the cross-sectional research design limits the extent to which cause-effect relations can be inferred. It is intuitively plausible that high performing organizations might be better able to invest in high performance logistics practices. The limitations of survey design did not allow for the capture of potentially important control variables. This is especially important in controlling for the interactions and influence of actions and performance by other functions in the organization. While this research sought generalizability across multiple manufacturing industries by using survey methodology, future research could apply other research approaches to focus on single industries or firms.

In addition, whereas our research captured objective measures of organizational performance from a sample of

firms, which significantly correlated with our perceptual measures, future research that obtains objective measures for logistics performance would be a substantial contribution to the logistics discipline. At this point, however, inventory turns (cost of goods sold/average inventory) is the only logistics performance item (from our survey) available for comparison in Compustat (with significant correlations between perceptual and objective data in our sample firms). Further, our research should be extended to capture logistics performance in the eyes of customers. While our research evaluated logistics managers’ perceptions of logistics performance as compared to competitors (i.e., logistics differentiation), customers should be surveyed to validate their perceptions. Another interesting avenue of research would be to incorporate the perceptions from managers in other functions (e.g., marketing, operations, and finance) of the performance of the logistics function.


Finally, our research sample consisted only of manufacturing organizations. It would be interesting to evaluate the individual path weights between logistics performance and its dimensions, as well as its impact on organizational performance, in other tiers in the supply chain, such as wholesalers, distributors, and retailers. Future research testing Figure 1 in these various contexts can provide opportunities to refine and improve the robustness of the measurement scales and strengthen the findings of our research.

APPENDIX A

PROFILE OF RESPONDING COMPANIES

Annual Sales Volume

Pre-

teste

Final

Testf Industry

Pre-

test

Final

Test

< $1 million 0% 0% Automotive 7% 4%

$1-50 million 8% 8% Medical/pharm. 8% 10%

$51-250 million 10% 11% Electronics 13% 15%

$251-500 million 18% 18% Industrial 15% 12%

$501 million-$1 billion 29% 26% Consumer 26% 29%

> $1 billion 34% 37% Chemicals/plastics 13% 10%

Appliances 0% 1%

Apparel/textiles 7% 4%

Agriculture 2% 3%

Other 10% 12% e105 pre-test organizations f336 final test organizations

APPENDIX B

MEASURES

Logistics Performance (range: Strongly Disagree - Strongly Agree)

Item

Q1 Our overall logistics performance is well above industry average. {newly developed}

Q2 In general, our logistics performance is excellent. {newly developed}

Q3 We are outstanding at performing our logistics activities. {newly developed}

Logistics Differentiation (range: Far Below Competitors – Far Above Competitors) For the following items, please rate your business unit’s performance on logistics activities in comparison to

your major competitors. If you are associated with a company that does not consist of business units or divisions, please answer the following based on your company.

Item {all items adapted from Bobbitt (2004)} Q4 Damage Free Deliveries.

Q5g Finished Goods Inventory.

Q6 Forecasting Accuracy.

Q7 Line Item Fill Rate.

Q8 Time Between Order Receipt and Delivery.

Q9g Time on Backorder.

Q10 Total Inventory Turns.

Q11 On-Time Delivery.


APPENDIX B (cont.)

Logistics Efficiency (range: Poor – Excellent) For the following items, please rate your business unit’s performance on logistics activities for the previous

fiscal year.

Item {all items adapted from Bobbitt 2004} Q12 Percent of Orders Shipped to Customers from the Primary Location Designated to Serve Those

Customers.

Q13 Line Item Fill Rate (percentage of order items the picking operation actually found).

Q14 Percent of Orders Shipped on Time.

Q15 Percent of Shipments Requiring Expediting.

Q16g Inventory Turns per Year.

Q17 Average Order Cycle Time (time in days between order receipt and order delivery).

Logistics Effectiveness (range: Much Worse – Much Better) For the following items, please rate your business unit’s actual performance compared to budgeted

performance, based on the previous fiscal year results.

Item {all items adapted from Bobbitt 2004}

Q18 Sales (Dollars).

Q19 Transportation Costs

Q20g Warehousing Costs

Q21 Inventory Costs.

Q22 Total Logistics Costs.

Organizational Performance (range: Far Below Competitors – Far Above Competitors) In your judgment, how did your BUSINESS UNIT perform relative to its major competitor in the previous fiscal year with respect to each criterion? If you are associated with a company that does not consist of business units or divisions, please answer the following based on your company.

Item {items adopted from Baker and Sinkula (1999) and Matsuno (2000)}

Q23g Overall Performance.

Q24 Market Share Growth in our Primary Market.

Q25 Sales Growth.

Q26 Percentage of New Product Sales Generated by New Products.

Q27 Return on Sales.

Q28 Return on Assets.

Q29 Return on Investments.

Global Reach (range: Strongly Agree - Strongly Disagree)

Item {items adopted from Fawcett et al. (1996)}

Q30 Your business unit locates specific production activities in countries that provide a comparative advantage.

Q31 Production facilities are placed in foreign countries to develop a positive image as a local player.

Q32 Top management emphasizes global manufacturing strategy within the overall corporate strategy.

Q33 Your global manufacturing approach has been formalized and incorporated into the firm’s competitive strategy.

gIndicates an item that was removed during scale purification.


NOTES

Amos (2005), AMOS 6.0., Spring House, PA: Amos Development Corporation. Anderson, James C. and David W. Gerbing (1988), “Structural Equation Modeling in Practice: A Review of the Two-Step Approach,” Psychological Bulletin, Vol. 103, No. 3, pp. 411-423. Bagozzi, Richard P. and Youjae Yi (1988), “On the Evaluation of Structural Equation Models,” Journal of the

Academy of Marketing Science, Vol. 16, No. 1, pp. 74-94. Barnard, C. (1939), The Functions of the Executive, Cambridge, MA: Harvard Business Press. Basak, Suleyman, Alex Shapiro, and Lucie Teplá (2006), “Risk Management with Benchmarking,” Management

Science, Vol. 52, No. 4, pp. 542-557. Bobbitt, L. Michelle (2004), “An Examination of the Logistics Leverage Process: Implications for Marketing Strategy and Competitive Advantage,” Unpublished Ph.D. Dissertation, The University of Tennessee. Brewer, Peter C. and Thomas W. Speh (2000), “Using the Balanced Scorecard to Measure Supply Chain Performance,” Journal of Business Logistics, Vol. 21, No. 1, pp. 75-93. Cameron, Kim (1986), “A Study of Organizational Effectiveness and its Predictors,” Management Science, Vol. 32, No. 1, pp. 87-112. Chamberlain, E. H. (1968), The Theory of Monopolistic Competition, Cambridge, MA: Harvard University Press. Cheng, Liang-Chieh and Curtis M. Grimm (2006), “The Application of Empirical Strategic Management Research to Supply Chain Management,” Journal of Business Logistics, Vol. 27, No. 1, pp. 1-56. Chin, Wynne W. (1998), “The Partial Least Squares Approach to Structural Equation Modeling,” in George A. Marcoulides, ed., Modern Methods for Business Research, Mahwah, NJ: Lawrence Erlbaum. Chin, Wynne W. and Peter R. Newsted (1999), “Structural Equation Modeling Analysis with Small Samples using Partial Least Squares,” in Rick H. Hoyle, ed., Strategies for Small Sample Research, Thousand Oaks, CA: Sage Publications, Inc., pp. 307-341. Chin, Wynne W. and Peter A. Todd (1995), “On the Use, Usefulness, and Ease of Ease of Structural Equation Modeling in MIS Research: A Note of Caution,” Management Information Systems Quarterly, Vol. 19, No. 2 (June), pp. 239-240. Chow, Garland, Trevor D. Heaver, and Lennart E. Henriksson (1994), “Logistics Performance: Definition and Measurement,” International Journal of Physical Distribution and Logistics Management, Vol. 24, No. 1, pp. 17-28. Churchill, Gilbert A., Jr. (1979), “A Paradigm for Developing Better Measures of Marketing Constructs,” Journal of

Marketing Research, Vol. 16, No. 1, pp. 64-73. Claycomb, Cindy, Richard Germain, and Cornelia Dröge (2000), “The Effects of Formal Strategic Marketing Planning on the Industrial Firm's Configuration, Structure, Exchange Patterns, and Performance,” Industrial

Marketing Management, Vol. 29, No. 3, pp. 219-234. Davis, Peter S. and Timothy L. Pett (2002), “Measuring Organizational Efficiency and Effectiveness,” Journal of

Management Research, Vol. 2, No. 2, pp. 87-97. Davis, T. C. (1950), “How the DuPont Organization Appraises Its Performance,” Financial Management Series, New York: American Management Association Treasurer's Department, No. 94, pp. 3-11.


Dess, Gregory G. and Richard B. Robinson, Jr. (1984), “Measuring Organizational Performance in the Absence of Objective Measures: The Case of the Privately-Held Firm and Conglomerate Business Unit,” Strategic

Management Journal, Vol. 5, No. 3, pp. 265-273. Diamantopoulos, Adamantios and Judy A. Siguaw (2006), “Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration,” British Journal of Management, Vol. 17, No. 4, pp. 263-282. Diamantopoulos, Adamantios and Heidi M. Winklhofer (2001), “Index Construction with Formative Indicators: An Alternative to Scale Development,” Journal of Marketing Research, Vol. 38, No. 2, pp. 269-277. Dillman, Don A. (2000), Mail and Internet Surveys: The Total Design Method, New York: John Wiley & Sons. Dröge, Cornelia and Richard Germain (2000), “The Relationship of Electronic Data Interchange with Inventory and Financial Performance,” Journal of Business Logistics, Vol. 21, No. 2, pp. 209-230. Dunn, Steven C., Robert F. Seaker, and Matthew A. Waller (1994), “Latent Variables in Business Logistics Research: Scale Development and Validation,” Journal of Business Logistics, Vol. 15, No. 2, pp. 145-172. Ellinger, Alexander E., Patricia J. Daugherty, and Scott B. Keller (2000), “The Relationship between Marketing/Logistics Interdepartmental Integration and Performance in U.S. Manufacturing Firms: An Empirical Study,” Journal of Business Logistics, Vol. 21, No. 1, pp. 1-22. Enslow, Beth, John Fontanella, Cheryl Harrity, and Lisa Higgins (2005), By the Numbers: Logistics, Houston, TX: American Productivity and Quality Center. Enz, Cathy A. (1988), “The Role of Value Congruity in Intraorganizational Power,” Administrative Science

Quarterly, Vol. 33, No. 2, pp. 284-304. Fawcett, Stanley E., Roger Calantone, and Sheldon R. Smith (1996), “An Investigation of the Impact of Flexibility on Global Reach and Firm Performance,” Journal of Business Logistics, Vol. 17, No. 2, pp. 167-196. Fisher, Marshall L. (1997), “What is the Right Supply Chain for Your Product?” Harvard Business Review, Vol. 75, No. 2, pp. 105-116. Flint, Daniel J., Everth Larsson, Britta Gammelgaard, and John T. Mentzer (2005), “Logistics Innovation: A Customer Value-Oriented Social Process,” Journal of Business Logistics, Vol. 26, No. 1, pp. 113-147. Ford, Jeffrey D. and Deborah A. Schellenberg (1982), “Conceptual Issues of Linkage in the Assessment of Organizational Performance,” Academy of Management Review, Vol. 7, No. 1, pp. 49-58. Fornell, Claes and Fred L. Bookstein (1982), “Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory,” Journal of Marketing Research, Vol. 19, No. 4, pp. 440-452. Fornell, Claes and David F. Larcker (1981a), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, Vol. 18, No. 1, pp. 39-50. Fornell, Claes and David F. Larcker (1981b), “Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics,” Journal of Marketing Research, Vol. 18, No. 3, pp. 382-388. Frankel, Robert, Dag Naslund, and Yemisi Bolumole (2005), “The ‘White Space’ of Logistics Research: A Look at the Role of Methods Usage,” Journal of Business Logistics, Vol. 26, No. 2, pp. 185-208. Golicic, Susan L., James H. Foggin, and John T. Mentzer (2003), “Relationship Magnitude and its Role in Interorganizational Relationship Structure,” Journal of Business Logistics, Vol. 24, No. 1, pp. 57-76.


Griffis, Stanley E., Martha Cooper, Thomas J. Goldsby, and David J. Closs (2004), “Performance Measurement: Measure Selection Based upon Firm Goals and Information Reporting Needs,” Journal of Business Logistics, Vol. 25, No. 2, pp. 95-118. Griffis, Stanley E., Thomas J. Goldsby, and Martha Cooper (2003), “Web-Based and Mail Surveys: A Comparison of Response, Data, and Cost,” Journal of Business Logistics, Vol. 24, No. 2, pp. 237-258. Griffis, Stanley E., Thomas J. Goldsby, Martha Cooper, and David J. Closs (2007), “Aligning Logistics Performance Measures to the Information Needs of the Firm,” Journal of Business Logistics, Vol. 28, No. 2, pp. 35-56. Haenlein, Michael and Andreas M. Kaplan (2004), “A Beginner’s Guide to Partial Least Squares Analysis,” Understanding Statistics, Vol. 3, No. 4, pp. 283-297. Helm, Sabrina (2005), “Designing a Formative Measure for Corporate Reputation,” Corporate Reputation Review, Vol. 8, No. 2, pp. 95-109. Holmberg, Stefan (2000), “A Systems Perspective on Supply Chain Measurements,” International Journal of

Physical Distribution and Logistics Management, Vol. 30, No. 10, pp. 847-868. Hoyle, Rich H. (1995), Structural Equation Modeling: Concepts, Issues, and Applications, Thousand Oaks, CA: Sage Publications, Inc. Hulland, John (1999), “Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies,” Strategic Management Journal, Vol. 20, No. 2, pp. 195-204. Hunt, Shelby D. and Robert M. Morgan (1995), “The Comparative Advantage Theory of Competition,” Journal of

Marketing, Vol. 59, No. 2, pp. 1-15. Jarvis, Cheryl B., Scott B. MacKenzie, and Philip M. Podsakoff (2003), “A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research,” Journal of Consumer Research, Vol. 30, No. 2, pp. 199-218. Johnson, Michael D., Andreas Herrmann, and Frank Huber (2006), “The Evolution of Loyalty Intentions,” Journal

of Marketing, Vol. 70, No. 2, pp. 122-132. Kannan, Vijay R. and Keah Choon Tan (2006), “Buyer-Supplier Relationships,” International Journal of Physical

Distribution and Logistics Management, Vol. 36, No. 10, pp. 755-775. Katz, D. and R. L. Kahn (1978), The Social Psychology of Organizations, New York: John Wiley & Sons. Keller, Scott B., Katrina Savitskie, Theodore P. Stank, Daniel F. Lynch, and Alexander E. Ellinger (2002), “A Summary and Analysis of Multi-Item Scales Used in Logistics Research,” Journal of Business Logistics, Vol. 23, No. 2, pp. 83-281. Kent, John L., Jr. and Daniel J. Flint (1997), “Perspectives on the Evolution of Logistics Thought,” Journal of

Business Logistics, Vol. 18, No. 2, pp. 15-29. Kotabe, Masaaki (1988), “Efficiency vs. Effectiveness Orientation of Global Sourcing: A Comparison of U.S. and Japanese Multinational Companies,” Academy of Management Executive, Vol. 12, (November) pp. 107-119. Lambert, Douglas M. and Renan Burduroglo (2000), “Measuring and Selling the Value of Logistics,” The

International Journal of Logistics Management, Vol. 11, No. 1, pp. 1-16. Lambert, Douglas M., Sebastián J. García-Dastugue, and Keely L. Croxton (2005), “An Evaluation of Process-Oriented Supply Chain Management Frameworks,” Journal of Business Logistics, Vol. 26, No. 1, pp. 25-51.


Lambert, Douglas M. and Terrance L. Pohlen (2001), “Supply Chain Metrics,” The International Journal of

Logistics Management, Vol. 12, No. 1, pp. 1-18. Langley, C. John, Jr. and Mary C. Holcomb (1992), “Creating Logistics Customer Value,” Journal of Business

Logistics, Vol. 13, No. 2, pp. 1-27. Larson, Paul D. and Robert F. Lusch (1990), “Quick Response Retail Technology: Integration and Performance Measurement,” The International Review of Retail, Distribution and Consumer Research, Vol. 1, No. 1, pp. 17-35. Lindell, Michael K. and David J. Whitney (2001), “Accounting for Common Method Variance in Cross-Selectional Research Designs,” Journal of Applied Psychology, Vol. 86, No. 1, pp. 114-121. Luo, Xueming, Rebecca J. Slotegraaf, and Xing Pan (2006), “Cross-Functional ‘Coopetition:’ The Simultaneous Role of Cooperation and Competition within Firms,” Journal of Marketing, Vol. 70, No. 2, pp. 67-80. Lynch, Daniel F., Scott B. Keller, and John Ozment (2000), “The Effects of Logistics Capabilities and Strategy on Firm Performance,” Journal of Business Logistics, Vol. 21, No. 2, pp. 47-68. Lynch, John G., Jr., Bobby J. Calder, Lynn W. Phillips, Alice M. Tybout, Joseph E. McGrath, and David Brinberg (1983), “The Role of External Validity in Theoretical Research/Response/Comment,” Journal of Consumer

Research, Vol. 10, No. 1, pp. 109-111. MacKenzie, Scott B., Philip M. Podsakoff, and Cheryl Burke Jarvis (2005), “The Problem of Measurement Model Misspecification in Behavioral and Organizational Research and Some Recommended Solutions,” Journal of

Applied Psychology, Vol. 90, No. 4, pp. 710-730. Mahoney, Thomas A. (1988), “Productivity Defined: The Relativity of Efficiency, Effectiveness, and Change,” in J. P. Campbell and R. J. Campbell, eds., Productivity in Organizations: New Perspectives from Industrial and

Organizational Psychology, San Francisco: Jossey-Bass. Manrodt, Karl B., David A. Durtsche, D. Michael Ledyard, and James S. Keebler (1999), Keeping Score:

Measuring the Business Value of Logistics in the Supply Chain, Oak Brook, IL: Council of Logistics Management. Matsuno, Ken, John T. Mentzer, and Aysegul Ozsomer (2002), “The Effects of Entrepreneurial Proclivity and Market Orientation on Business Performance,” Journal of Marketing, Vol. 66, No. 3, pp. 18-32. Matsuno, Ken, John T. Mentzer, and Joseph O. Rentz (2000), “A Refinement and Validation of the MARKOR Scale,” Journal of the Academy of Marketing Science, Vol. 28, No. 4, pp. 527-539. Menon, Anil, Sundar G. Bharadwaj, and Roy Howell (1996), “The Quality and Effectiveness of Marketing Strategy: Effects of Functional and Dysfunctional Conflict in Intraorganizational Relationships,” Journal of the Academy of

Marketing Science Vol. 24, No. 4, pp. 299-313. Mentzer, John T. and Daniel J. Flint (1997), “Validity in Logistics Research,” Journal of Business Logistics, Vol. 18, No. 1, pp. 199-216. Mentzer, John T., Daniel J. Flint, and John L. Kent (1999), “Developing a Logistics Service Quality Scale,” Journal

of Business Logistics, Vol. 20, No. 1, pp. 9-32. Mentzer, John T. and Brenda Ponsford Konrad (1991), “An Efficiency/Effectiveness Approach to Logistics Performance,” Journal of Business Logistics, Vol. 12, No. 1, pp. 33-62. Mentzer, John T. and Lisa R. Williams (2001), “The Role of Logistics Leverage in Marketing Strategy,” Journal of

Marketing Channels, Vol. 8, pp. 29-47.


Miller, D. (1981), “Toward a New Contingency Approach: The Search for Organizational Gestalts,” Journal of

Management Studies, Vol. 18, No. 1, pp. 1-26. Min, Soonhong and John T. Mentzer (2004), “Developing and Measuring Supply Chain Management Concepts,” Journal of Business Logistics, Vol. 25, No. 1, pp. 63-100. Mitchell, V. (1994), “Using Industrial Key Informants: Some Guidelines,” Journal of the Market Research Society, Vol. 36, No. 2, pp. 139-144. Morash, Edward A., Cornelia L. M. Dröge, and Shawnee K. Vickery (1996), “Strategic Logistics Capabilities for Competitive Advantage and Firm Success,” Journal of Business Logistics, Vol. 17, No. 1, pp. 1-22. Morgan, Robert M. and Shelby D. Hunt (1994), “The Commitment-Trust Theory of Relationship Marketing,” Journal of Marketing, Vol. 58, No. 3, pp. 20-38. Novack, Robert A., Lloyd M. Rinehart, and C. John Langley, Jr. (1994), “An Internal Assessment of Logistics Value,” Journal of Business Logistics, Vol. 15, No. 1, pp. 113-152. Ostroff, Cheri and Neal Schmitt (1993), “Configurations of Organizational Effectiveness and Efficiency,” Academy

of Management Journal, Vol. 36, No. 6, pp. 1345-1361. Pearce, John A., II, D. Keith Robbins, and Richard B. Robinson, Jr. (1987), “The Impact of Grand Strategy and Planning Formality on Financial Performance,” Strategic Management Journal, Vol. 8, No. 2, pp. 125-134. Podsakoff, P. M., S. B. MacKenzi, J. Y. Lee, and N. P. Podsakoff (2003), “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” Journal of Applied Psychology

Bulletin, Vol. 88, No. 5, pp. 879-903. Richey, R. Glenn, Patricia J. Daugherty, and Anthony S. Roath (2007), “Firm Technological Readiness and Complementarity: Capabilities Impacting Logistics Service Competency and Performance,” Journal of Business

Logistics, Vol. 28, No. 1, pp. 195-228. Rust, Roland T., Chol Lee, and Ernest Valente, Jr. (1995), “Comparing Covariance Structure Models: A General Methodology,” International Journal of Research in Marketing, Vol. 12, No. 4, pp. 279-291. Rutner, Stephen M. and C. John Langley, Jr. (2000), “Logistics Value: Definition, Process and Measurement,” The

International Journal of Logistics Management, Vol. 11, No. 2, pp. 73-82. Selldin, Erik and Jan Olhager (2007), “Linking Products with Supply Chains: Testing Fisher’s Model,” Supply

Chain Management: An International Journal, Vol. 12, No. 1, pp. 42-51. Singhal, Vinod R. and Kevin B. Hendricks (2002), “How Supply Chain Glitches Torpedo Shareholder Value,” Supply Chain Management Review, Vol. 6, No. 1 (January/February), pp. 18-24. Sink, D. S. (1985), Productivity Management: Planning, Measurement, and Evaluation, Control, and Improvement, New York: John Wiley & Sons. Slater, Stanley F. and John C. Narver (1994), “Does Competitive Environment Moderate the Market Orientation-Performance Relationship?” Journal of Marketing, Vol. 58, No. 1, pp. 46-55. Smith, Carlos D. (2000), “Assessing the Value of Improved Forecasting Management,” Unpublished Ph.D.

Dissertation, The University of Tennessee. Stahl, Michael J. and Gregory M. Bounds (1991), Competing Globally Through Customer Value, New York: Qorum Book.


Stank, Theodore P., Beth R. Davis, and Brian S. Fugate (2005), “A Strategic Framework for Supply Chain Oriented Logistics,” Journal of Business Logistics, Vol. 26, No. 2, pp. 27-46. Stank, Theodore P., Thomas J. Goldsby, Shawnee K. Vickery, and Katrina Savitskie (2003), “Logistics Service Performance: Estimating its Influence on Market Share,” Journal of Business Logistics, Vol. 24, No. 1, pp. 27-56. Stapleton, Drew, Joe B. Hanna, Steve Yagla, Jay Johnson, and Dan Markussen (2002), “Measuring Logistics Performance Using the Strategic Profit Model,” The International Journal of Logistics Management, Vol. 13, No. 1, pp. 89-107. Steers, R. M. (1975), “Problems in the Measurement of Organizational Effectiveness,” Administrative Science

Quarterly, Vol. 20, No. 4, pp. 546-558. Stephenson, P. Ronald (1976), “Strategic Analysis of Wholesale Distribution: A Study of the Medical Supply Industry,” Industrial Marketing Management, Vol. 5, No. 1, pp. 37-44. Swink, Morgan and Michael Song (2007), “Effects of Marketing-Manufacturing Integration on New Product Development Time and Competitive Advantage,” Journal of Operations Management, Vol. 25, pp. 203-217. Tan, Keah-Choon, Vijay R. Kannan, Robert B. Handfield, and Soumen Ghosh (1999), “Supply Chain Management: An Empirical Study of its Impact on Performance,” International Journal of Operations & Production Management, Vol. 19, No. 10, pp. 1034-1052. Van der Meulen, P. R. H. and G. Spijkerman (1985), “The Logistics Input-Output Model and its Application,” International Journal of Physical Distribution and Materials Management, Vol. 15, No. 3, pp. 17-25. Venkatraman, N. and Vasudevan Ramanujam (1986), “Measurement of Business Performance in Strategy Research: A Comparison of Approaches,” Academy of Management Review, Vol. 11, No. 4, pp. 801-814. Waller, Matthew A. and Robert A. Novack (1995), “The Effects of Judgmental Structure, Complexity, and Consistency on Managerial Performance,” Journal of Business Logistics, Vol. 16, No. 2, pp. 137. Wernerfelt, Birger (1984), “A Resource-Based View of the Firm,” Strategic Management Journal, Vol. 5, No. 2, pp. 171-180. Williamson, Kenneth C., Daniel M. Spitzer, Jr., and David J. Bloomberg (1990), “Modern Logistics Systems: Theory and Practice,” Journal of Business Logistics, Vol. 11, No. 2, pp. 65-86. Zacharia, Zach G. and John T. Mentzer (2004), “Logistics Salience in a Changing Environment,” Journal of

Business Logistics, Vol. 25, No. 1, pp. 187-210.


ABOUT THE AUTHORS

Brian S. Fugate (Ph.D. University of Tennessee) is an Assistant Professor of Supply Chain Management in the

Department of Management at Colorado State University. Brian received his Ph.D. from the University of Tennessee. His research interests include the performance implications of logistics’ role in integrating demand and supply, supply chain boundary spanners, and supply chain coordination. His research has appeared in the Journal of

Business Logistics, Journal of Operations Management, International Journal of Physical Distribution and Logistics

Management, and Supply Chain Management Review, and multiple conference proceedings. Prior to pursuing the Ph.D., Brian worked in logistics and industrial engineering at John Deere, Allied Signal, and Delta Airlines.

John T. (Tom) Mentzer (Ph.D. Michigan State University) is a Chancellor’s Professor and the Harry J. and

Vivienne R. Bruce Excellence Chair of Business in the Department of Marketing and Logistics at the University of Tennessee. He has published eight books and more than 200 articles and papers in the Journal of Business Logistics, Journal of Marketing, Harvard Business Review, 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 journals. He was the 2004 recipient of the Council of Logistics Management Distinguished Service Award.

Theodore P. Stank (Ph.D. University of Georgia) is Department Head for Marketing and Logistics and John H.

Dove Distinguished Professor of Logistics at the University of Tennessee. He is co-author of 21st Century Logistics:

Making Supply Chain Integration a Reality, Logistical Management, and has published numerous articles in the areas of logistics strategy, customer relevance, and internal and external integration in various journals, including the Business Horizons, Journal of Business Logistics, Journal of Operations Management, Management Science, Supply Chain Management Review, and Transportation Journal. Contact author: Brian S. Fugate; E-mail: [email protected]

logistics performance efficiency, effectiveness, and differentiation.pdf

Documents