M
A
R
K
E
T
I
N
G
S
C
I
E
N
C
E
I
N
S
T
I
T
U
T
E
Reports Knowledge as a Strategic Resource in Logistics and Purchasing By G. Tomas M. Hult, S. Tamer Cavusgil, and Roger J. Calantone
Creating a Superior Customer-Relating Capability By George S. Day
Marketing Meets Design Conference summary by Lily Aguirre Just and Rommel Salvador
Bottoms up! How Container Shapes Influence Pouring and Consumption Volume By Brian Wansink and Koert van Ittersum
Ten Lessons for Improving Service Quality By Leonard L. Berry, A. Parasuraman, and Valarie A. Zeithaml
The Relevance of Rigor By Donald R. Lehmann
2 0 0 3 W O R K I N G P A P E R S E R I E S I S S U E N O.
O N E
0 3-0 0 1
M
A
R
K
E
T
I
N
G
S
C
I
E
N
C
E
I
N
S
T
I
T
U
T
E
Reports Executive Director Donald R. Lehmann Research Director Ross Rizley Editorial Director Susan Keane Publication Design Laughlin/Winkler, Inc.
The Marketing Science Institute supports academic research for the development—and practical translation—of leadingedge marketing knowledge on issues of importance to business performance. Topics are identified by the Board of Trustees, which represents MSI member corporations and the academic community. MSI supports academic studies on these issues and disseminates findings through conferences and workshops, as well as through its publications series. Marketing Science Institute 1000 Massachusetts Avenue Cambridge, MA 02138-5396 Phone: 617.491.2060 Fax: 617.491.2065 Web: www.msi.org E-mail:
[email protected] MSI Reports is published quarterly by the Marketing Science Institute. It is not to be reproduced or published, in any form or by any means, electronic or mechanical, without written permission. The views expressed in MSI Reports are those of the authors.
2 0 0 3 W O R K I N G P A P E R S E R I E S I S S U E N O.
O N E
0 3-0 0 1
MSI Reports © 2003 Marketing Science Institute All rights reserved.
Subscriptions Annual subscriptions to MSI Reports can be placed online at www.msi.org. Questions regarding subscriptions may be directed to
[email protected]. Single reports Articles in the 2003 MSI Reports are available in downloadable (PDF) format at www.msi.org. Past reports MSI working papers published before 2003 are available as individual hard-copy reports; many are also available in downloadable (PDF) format. To order, go to www.msi.org. Corporate members MSI member company personnel receive MSI Reports (PDF and print versions) free of charge. Academic members Academics may apply for free access to PDF (downloadable) versions of articles in the 2003 MSI Reports and other working papers and for special rates on MSI print publications. For more information and to apply, go to “Qualify for academic membership” at www.msi.org. Classroom use Upon written request, MSI working papers may be copied for one-time classroom use free of charge. Please contact MSI to obtain permission.
Search for publications See the searchable publications database at www.msi.org. Submissions MSI will consider a paper for inclusion in MSI Reports, even if the research was not originally supported by MSI, if the paper deals with a priority subject, represents a significant advance over existing literature, and has not been widely disseminated elsewhere. Only submissions from faculty members or doctoral students working with faculty advisors will be considered. “MSI Working Paper Guidelines” and “MSI 2002–2004 Research Priorities” are available in the Research section of www.msi.org. Publication announcements To sign up to receive notices about MSI publications, go to www.msi.org. Change of address Send old and new address information to
[email protected].
W o r k i n g
P a p e r
Knowledge as a Strategic Resource in Logistics and Purchasing G. Tomas M. Hult, S. Tamer Cavusgil, and Roger J. Calantone
In complex supply chains, knowledge may be a key strategic resource. This study models the “knowledge path” in the marketing functions of logistics and purchasing, and assesses impact on speed, quality, cost, and flexibility in order fulfillment processes.
G. Tomas M. Hult is Director, Center for International Business Education and Research
Report Summary A firm manufactures automobiles in its Michigan plant, using materials and parts purchased from a large number of suppliers, ranging from carburetors to body panels. These suppliers are located across the globe, and delivery ranges from a few hours to 240 days. Each day, enough materials and parts need to be available at the plant to produce up to 356 automobiles.
and Associate Professor of Marketing and Supply Chain Management,
What is a critical strategic resource in this complex process?
S. Tamer Cavusgil is University Distinguished Faculty and holder of the J.W. Byington Chair in Global Marketing, and Roger J. Calantone is Director, Broad Information Technology
Knowledge, many would argue. In fact, supply chain members’ combined information and experience may well be the most significant source of value creation in this network. Consider that this knowledge pool is idiosyncratic to the specific chain and, thus, difficult to imitate—and knowledge emerges as a key competitive advantage.
Center and Broad Professor of Marketing, all at The Eli Broad Graduate School of Management, Michigan State University.
The view that knowledge is a strategic resource within the firm is relatively new; as with any emerging paradigm, model-building and empirical testing are critical. The knowledge paradigm—encompassing a complex network of individual experiences, information, routines, and capabilities—presents particular challenges. M A R K E T I N G
S C I E N C E
I N S T I T U T E
In this study, Tomas Hult, Tamer Cavusgil, and Roger Calantone examine the strategic role of knowledge in the supply chain. They delineate specific components of the knowledge resource, and outline a “knowledge path-flow” model for the logistics and purchasing functions. They test their model on a sample of 545 logistics and 368 purchasing professionals from manufacturing organizations. To assess outcomes, they examine speed, quality, cost, and flexibility in order fulfillment processes. Overall, their findings support the notion that knowledge can be a key strategic resource in the supply chain. They find that in both the logistics and purchasing functions, organizational memory— that is, members’ explicit information about, and experience and familiarity with, supply chain activities—is a key element of the function’s knowledge base. Further, they find, knowledge quality drives knowledge use; quality also determines how deeply knowledge is embedded in current practices. This finding suggests that supply chains, usually comprised of several firms, should 3
develop accessible mechanisms for sharing highquality knowledge. Which knowledge constructs ultimately ensure better performance outcomes in order fulfillment processes? The researchers posit that an enhanced ability to respond to a customer’s (i.e., a purchasing manager’s) product needs or an increased learning capacity would ultimately improve speed, quality, cost, and flexibility. They find that, in the purchasing function, order fulfillment processes are enhanced by the ability of sellers to immediately respond to the purchasers’ product needs. In the logistics function, on the other hand, outcomes are enhanced by supply chain members’ increased skills and knowledge, typically reflected in cutting-edge products and innovative solutions to problems. In the turbulence facing many industries today, it is increasingly important to build understanding of the unique resources that facilitate the delivery of products and services. This study provides empirical evidence that knowledge is a key strategic resource in that process. ■
Introduction The 1990s saw an increased focus on organizational learning, the learning organization, and the knowledge-creating company (Sinkula 1994; Slater and Narver 1995; Nonaka 1994). Companies such as Corning, General Electric, and Xerox applied knowledge initiatives to gain a competitive edge, and scholars theorized about how knowledge creates this competitive edge in varied settings (Marquardt and Reynolds 1994; Grant 1996a). As an intangible strategic resource, knowledge is crucial to an organization’s efforts to create value that is unique, inimitable, and nontransferable ( Jensen and Meckling 1992). In fact, Grant (1996b) argues that all human productivity is knowledge dependent, and technical tools and machines are simply embodiments of knowledge. M A R K E T I N G
S C I E N C E
I N S T I T U T E
Therefore, an organization’s knowledge base, capacity to develop new knowledge, and effectiveness in using knowledge represent the primary means to achieve a sustainable competitive advantage in the marketplace (cf. Hansen 2002). Our research explores how and why knowledge, as a specific organizational resource, can lead to sustained competitive advantages (Penrose 1959; Wernerfelt 1984). By focusing on knowledge, we follow a number of scholars who have departed from the pursuit of a theory of multiple resources to focus on a single critical resource: knowledge (e.g., Grant 1996a, b, 1997; Hedlund 1994; Liebeskind 1996; Spender 1996). In particular, the influence of a dynamic environment and rapid advances in information technology during the last decade have given rise to the recognition that knowledge “can sustain a resource barrier” (i.e., knowledge is the most critical strategic resource that an organization can possess) (Wernerfelt 1984, p. 175) and can be used to achieve a sustainable competitive advantage (Glazer 1991; Glazer and Weiss 1993). Knowledge is viewed as the only resource that is perfectly heterogeneous, imperfectly mobile, and asymmetrically distributed among firm-level competitors (cf. Barney 1991; Hunt and Morgan 1995). From this understanding of knowledge as a strategic resource, the knowledge-based view of the firm has emerged (e.g., Grant 1996a). An outgrowth of several streams of study, the knowledge-based view is seen primarily as an offspring of the resource-based view of the firm (e.g., Wernerfelt 1984). It depicts an organization as an “institution for integrating knowledge” (Grant 1996a, p. 109). Building on Simon’s (1957, 1991) notion of bounded rationality, the knowledgebased view also asserts that members of organizations must specialize in certain areas of knowledge. An organization’s charge is to draw on individuals’ collective wisdom in such a way as to perform important tasks well (e.g., providing the goods and services that customers want). Continual success in these tasks helps ensure the organization’s long-term prosperity. 4
In this paper, we expand the application of the knowledge-based view to the supply chain, defined as a “network of facilities and activities that performs the functions of product development, procurement of material from suppliers, the movement of materials between facilities, the manufacturing of products, the distribution of finished goods to customers, and after-market support for sustainment” (Mabert and Venkataraman 1998, p. 538). Because of their complexity, supply chains represent an area in which knowledge initiatives can create unique competitive advantages (Carter and Narasimhan 1996; Hult 1998; Hult et al. 2000; Niraj, Gupta, and Narasimhan 2001). For example, FedEx recently spent $100 million to reorganize its supply chain structure, and since 1986 UPS has spent $9 billion on information technology in an effort to improve supply chain performance (Farhoomand and Ng 2000). Conversely, an organization’s ability to provide value to customers can be severely impeded by dysfunctional supply chains (Venkatesh, Kohli, and Zaltman 1995). For example, a supply chain system that is too decentralized or too centralized often does not achieve maximum benefits to the organization as a whole; a focus on knowledge as a resource facilitates the achievement of the appropriate structural and process focus of the chain. Two marketing functions that are critical to the supply chain are logistics—“the distribution of finished goods to customers” and purchasing— “procurement of material from suppliers” (Mentzer, Flint, and Hult 2001; Cannon and Homburg 2001). In this study, we develop and empirically examine a model of knowledge and its effect on performance in logistics and purchasing. Drawing on the knowledge-based view, we assimilate a set of knowledge constructs as value creation elements within logistics and purchasing functions of supply chains. We examine knowledge at the organizational level, where it is viewed as a strategic resource, rather than at the individual level, where it may be viewed as an asset. M A R K E T I N G
S C I E N C E
I N S T I T U T E
Specifically, our research questions for this study are: What are the strategically important knowledge constructs in the marketing functions of logistics and purchasing? How are they integrated to shape important outcomes in the supply chain?
Modeling the Knowledge Path We define knowledge broadly as credible information or experience (or both) that is of potential value to a marketing function (in the context of our study, logistics and purchasing). According to Grant (1996a), five characteristics are pertinent here: specialization in knowledge acquisition, transferability, capacity for aggregation, appropriability, and the knowledge requirements of production. Our contention is that these five characteristics build on one another to create value as a function of the utilization of knowledge (cf. Machlup 1980). “Hypothesized Model” illustrates the proposed model of the development of knowledge as a strategic marketing resource in supply chains, and lists the hypotheses that underlie the model. As shown, our model builds a path flow of knowledge elements using the latter four of the five characteristics proposed by Grant (1996a). Grant’s first knowledge characteristic—specialization in knowledge acquisition—was omitted from the study based on the assumption that supply chain participants contribute to knowledge acquisition according to their distinctive competencies (cf. Hult et al. 2000). This is done to create a base of critical knowledge (cf. Heide and John 1992) used to pursue goals shared by the supply chain participants (e.g., Cespedes 1996; Leavitt 1965). The latter four of Grant’s elements help form the strategic resource of knowledge. Grant’s first element, which can be described as an information processing activity, has already been studied extensively in marketing (e.g., Hult et al. 2000; Jaworski and Kohli 1993; Rindfleisch and Moorman 2001). Our first knowledge construct, transferability of knowledge, is the knowledge base for marketing 5
Hypothesized Model Transferability
Capacity for
Appropriability
Aggregation
Knowledge
Logistics/
Requirements
Purchasing
of Production
Performance
Speed of Process Organizational Memory (OM)
H1b
Tacitness of Knowledge (TOK)
Accessibility of Knowledge (AOK)
H4a
Knowledge Use (KU)
H2b
Quality of Knowledge (QOK)
H5a H5b
H6a H6b
H4b
H2a
H3a
Knowledge Filtering (KF)
H8a
H1a
Responsiveness to Product Needs (R)
H7b
H3b
Learning Capacity (LC)
Quality of Process
H8b H9b
H7a
Knowledge Intensity (KI)
H9a
H8c H9c
H9d
Cost of Process
H8d
Flexibility of Process H1: Organizational memory has a positive effect on (a) accessibility of knowledge and (b) quality of knowledge. H2: Tacitness of knowledge has a positive effect on (a) accessibility of knowledge and (b) quality of knowledge. H3: Knowledge filtering has a positive effect on (a) accessibility of knowledge and (b) quality of knowledge. H4: Accessibility of knowledge has a positive effect on (a) knowledge use and (b) knowledge intensity. H5: Quality of knowledge has a positive effect on (a) knowledge use and (b) knowledge intensity. H6: Knowledge use has a positive effect on (a) responsiveness to product needs and (b) learning capacity. H7: Knowledge intensity has a positive effect on (a) responsiveness to product needs and (b) learning capacity. H8: Responsiveness to product needs has a positive effect on (a) speed, (b) quality, (c) cost, and (d) flexibility of the order fulfillment process. H9: Learning capacity has a positive effect on (a) speed, (b) quality, (c) cost, and (d) flexibility of the order fulfillment process.
activities and affects the next construct, capacity for aggregation of knowledge, which is a form of knowledge readiness for the marketing function. Capacity for aggregation, in turn, is proposed to have a direct effect on the appropriability of knowledge, the importance participants in the marketing function place on knowledge. Finally, appropriability is proposed to have a direct effect on the knowledge requirements of production. Specifically, as greater importance is placed on knowledge, the marketing function will be better able to respond to product needs and will increase their own knowledge and skills. M A R K E T I N G
S C I E N C E
I N S T I T U T E
Transferability We propose that transferability—the “knowledge base” for marketing activities—is comprised of three elements: organizational memory (Moorman 1995), tacitness of knowledge (Madhavan and Grover 1998), and knowledge filtering (i.e., into levels of importance; cf. Sinkula 1994). For example, knowing how to do something (tacitness of knowledge) and knowing about something (explicit knowledge stored in organizational memory) each represents operational aspects that are critical in the supply chain. While explicit knowledge is revealed in its ease of 6
communication (Grant 1996a), tacit knowledge is revealed through its application (Kogut and Zander 1993). Further, within each marketing function (for our purposes, logistics and purchasing) and perhaps also by each individual participant in the supply chain, knowledge is filtered for importance and applicability (e.g., Brown and Duguid 2001). Capacity for aggregation The capacity for aggregration of knowledge describes the “knowledge readiness” of the function: Is the function adequately prepared to use knowledge effectively and efficiently in their activities? Critical to this capability is the accessibility and quality of supply chain knowledge (e.g., Montgomery and Weinberg 1979; O’Reilly 1982). Specifically, the construct of accessibility of knowledge addresses issues such as: How accessible is the knowledge to the supply chain participants? Is it provided in the appropriate format and at the appropriate time? Quality of knowledge focuses on the degree to which the knowledge obtained provides an important “knowledge component” that can potentially have an integral effect on supply chain activities. Since we predict that a specific marketing function’s ability to transfer knowledge will affect its capacity for aggregation of knowledge, we predict that organizational memory, tacitness of knowledge, and knowledge filtering will positively affect accessibility and quality of knowledge (see H1, H2, and H3 in the model). Appropriability The term appropriability was introduced by Grant (1996b) to refer to the ability of an owner of a knowledge resource to receive a return equal to the value created by that resource. Interestingly, to its advantage, much of supply chain knowledge is idiosyncratic to the particular chain and its constituency groups (such as logistics and purchasing). As more knowledge is created in the system, supply chain participants will place increased operational importance, or value, on that knowledge, and will see it as more important to operational effectiveness. Thus, they will tend to increase the use of knowledge (e.g., Deshpandé and Zaltman 1982; Menon and Varadarajan 1992) M A R K E T I N G
S C I E N C E
I N S T I T U T E
and knowledge intensity (i.e., depth of knowledge in existing practices; Autio, Sapienze, and Almeida 2000). It follows, then, that the aggregation of knowledge, that is, its accessibility and quality, will directly affect its use and depth (often in an autocatalytic manner) (see H4 and H5 in the model). Knowledge requirements of production Building on Leavitt (1965), almost tautologically, a supply chain is created for the specific purpose of facilitating production and distribution not only of products and services, but of knowledge, via collaborative communication mechanisms (Mohr, Fisher, and Nevin 1996). We capture the knowledge requirements of production in two capabilities: responsiveness to customers’ product needs ( Jaworski and Kohli 1993) and learning capacity, that is, members’ increased skills and knowledge, typically reflected in innovative and cutting-edge products (Hurley and Hult 1998). Thus, we expect that knowledge use and intensity will positively affect the supply chains’ responsiveness to customers’ product needs and the supply chain’s learning capacity (see H6 and H7 in the model). Performance outcomes Knowledge as a strategic resource should ultimately have several effects on performance in supply chains. Specifically, we would expect performance to increase as responsiveness ( Jaworski and Kohli 1993) and learning capacity (Cohen and Levinthal 1990) increase in magnitude.The operations management literature (e.g., Anderson, Cleveland, and Schroeder 1989; Boyer and Pagell 2000; McKone, Schroeder, and Cua 2001; Ward et al. 1998; Youndt et al. 1996) suggests speed, quality, cost, and flexibility are critical performance indicators in logistics and purchasing. We focus on the order fulfillment process (cf. Hult, Ketchen, and Nichols 2002) and predict that responsiveness to product needs and learning capacity will positively affect the speed, quality, cost, and flexibility of the order fulfillment process (see H8 and H9 in the model).
Testing the Model Samples To examine our model, we collected two samples 7
Data Collection and Sample Logistics Sample 4,000 professionals contacted via e-mail for online survey
Response
Averages
545 respondents (16.9% response rate)
Firm age: 52 years # Employees: 11,695 Int’l activities: 25%
Response
Averages
368 respondents (15.6% response rate)
Firm age: 55 years # Employees: 3,080 Int’l activities: 24%
Source: Council of Logistics Management Purchasing Sample 1,300 professionals contacted via e-mail for online survey 1,700 professionals contacted via regular mail for online survey Source: Institute of Supply Management
Correlations Among Variables in Overall Sample (n = 913) Average
Average
within-
within-
construct
construct
correlations correlations (logistics)
(purchasing)
OM
TOK
KF
AOK
QOK
KU
OM
.73
.74
1.00
TOK
.56
.55
.43
1.00
KF
.67
.73
.36
.42
1.00
AOK
.81
.80
.62
.54
.49
1.00
QOK
.76
.80
.64
.49
.52
.76
1.00
KU
.59
.63
.48
.42
.41
.52
.61
1.00
KI
.84
.81
.65
.52
.50
.63
.73
.56
1.00
R
.57
.59
.33
.37
.31
.38
.41
.41
.37
1.00
LC
.65
.74
.44
.43
.47
.43
.49
.43
.50
.34
1.00
S
.54
.56
.29
.39
.35
.38
.42
.35
.40
.33
.37
1.00
Q
.66
.70
.37
.37
.46
.37
.43
.38
.44
.40
.39
.67
1.00
C
.59
.67
.37
.42
.46
.42
.50
.38
.47
.35
.41
.63
.66
1.00
F
.71
.74
.33
.39
.40
.40
.44
.42
.44
.35
.41
.63
.65
.64
from the outbound (logistics) and inbound (purchasing) activities of the supply chain. Key informants responded to a survey that addressed knowledge-related organizational-level phenomena. The unit of analysis for both the logistics and purchasing functions was the order M A R K E T I N G
S C I E N C E
I N S T I T U T E
KI
R
LC
S
Q
C
F
1.00
fulfillment process for a sample of executives. Both the logistics and the purchasing samples were restricted to manufacturing organizations (none of the logistics professionals in our sample came from the same organizations as any of the purchasing professionals). 8
Summary Statistics of Measurement Analysis Logistics Sample (n = 545) Variable
Mean
Standard Deviation
Variance Extracted
Composite Reliability
Factor Loadings
Fit Statistics for the Logistics Sample
OM
5.41
1.24
74.0%
.92
.58 to .94
χ2
8,460.16
TOK KF AOK
3.78 4.78 4.93
1.30 1.17 1.36
59.8% 59.0% 82.0%
.86 .92 .93
.74 to .83 .75 to .90 .87 to .95
Degrees of Freedom DELTA2 RNI
1,146
QOK KU KI
5.15 5.44 4.84
1.11 .98 1.52
71.8% 59.5% 82.7%
.93 .90 .93
.77 to .92 .64 to .85 .85 to .96
CFI TLI RMSEA
R
5.74
1.02
64.3%
.83
.59 to .97
LC S
4.84 4.87
1.22 1.13
66.0% 57.7%
.88 .79
.59 to .92 .49 to .87
Q C F
4.93 4.47 4.62
1.09 1.14 1.16
63.0% 61.3% 69.7%
.83 .82 .87
.60 to .90 .53 to .95 .64 to .94
.91 .91 .91 .91 .10
Purchasing Sample (n = 368) Variable
Mean
Standard Deviation
Variance Extracted
Composite Reliability
Factor Loadings
Fit Statistics for the Purchasing Sample χ2
OM TOK
4.93 3.43
1.22 1.21
74.8% 59.5%
.92 .85
.67 to .96 .60 to .95
Degrees of Freedom
8,337.83 1,146
KF AOK QOK KU
4.33 4.51 4.57 4.98
1.19 1.37 1.25 1.01
67.4% 82.3% 82.6% 66.0%
.93 .93 .96 .92
.65 to .91 .86 to .95 .84 to .94 .75 to .91
DELTA2 RNI CFI TLI
.91 .91 .91 .91
KI R LC S
4.13 5.15 4.31 4.61
1.42 .98 1.48 1.06
80.3% 59.0% 74.0% 52.3%
.92 .80 .92 .76
.84 to .93 .50 to .89 .69 to .95 .57 to .79
RMSEA
.12
Q C F
4.57 4.27 4.36
1.15 1.15 1.14
69.0% 64.0% 65.0%
.87 .84 .85
.69 to .90 .67 to .87 .66 to .88
The table on page 8 describes our data collection process for each of the two samples.
robust across samples (Appendix A identifies which indicators were deleted).
Measures Established and new scales were used to measure the constructs; they are described fully in Appendix A.The correlations among the study variables are presented on page 8 and the results of the measurement analysis are presented above. Appendix B describes the measurement purification process in detail. Overall, the 13 constructs and a purified set of 51 indicators were found to be reliable, valid, and
Results We tested the path-flow of knowledge described in the hypothesized model through structural equation modeling (SEM) using LISREL 8.53 ( Jöreskog and Sörbom 1996; Jöreskog et al. 2000). Using the 51 purified items as indicators of the constructs, we tested the nine hypotheses and their subsets in the logistics sample and in the purchasing sample. We then tested potential asymmetries in
M A R K E T I N G
S C I E N C E
I N S T I T U T E
9
Summary Results of Hypothesis Testing Parameter Estimates from SEM
Standardized Betas from Regression
Purchasing (n = 368)
Logistics (n = 545)
Hypothesis Result
Hypothesis
Logistics (n = 545)
Purchasing (n = 368)
H1a: OM➔AOK
.42***
.53***
.40***
.50***
H1b: OM➔QOK
.49***
.53***
.43***
.49***
Supported
H2a: TOK➔AOK
.34***
.31***
.26***
.27***
Supported
Supported
H2b: TOK➔QOK
.15***
.10**
.21***
.17***
Supported
H3a: KF➔AOK
.21***
.12***
.26***
.15***
Supported
H3b: KF➔QOK
.31***
.33***
.29***
.26***
Supported
H4a: AOK➔KU
.22***
.15**
.14**
.11
H4b: AOK➔KI
.27***
.16***
.20***
.17**
Supported
H5a: QOK➔KU
.58***
.54***
.53***
.49***
Supported
H5b: QOK➔KI
.49***
.67***
.55***
.63***
H6a: KU➔R
.46***
.36***
.34***
.12
H6b: KU➔LC
.30***
.20***
.22***
.15**
H7a: KI➔R
.10**
.49***
.11*
.26***
Supported
H7b: KI➔LC
.30***
.43***
.33***
.43***
Supported
H8a: R➔S
.17***
.74***
.26***
.22***
Supported
H8b: R➔Q
.36***
.91***
.36***
.22***
Supported
H8c: R➔C
.20***
.75***
.27***
.20***
Supported
H8d: R➔F
.15***
.80***
.29***
.20***
Supported
H9a: LC➔S
.36***
.11*
.25***
.31***
Supported
H9b: LC➔Q
.30***
–.11**
.24***
.36***
Partially supported
H9c: LC➔C
.38***
.21***
.22***
.38***
Supported
H9d: LC➔F
.38***
.01
.32***
.37***
Partially supported
Partially supported
Supported Partially supported Supported
Fit Statistics χ2
9,675.25
9,228.62
Adjusted R2-range:
Adjusted R2-range:
df
1,199
1,199
.15 to .50
.11 to .55
DELTA2, RNI, CFI
.90
.90
RMSEA
.11
.12
*** p < .01 ** p < .05 * p < .10
results between the logistics and purchasing analyses. Overall, 42 of the 44 hypothesized paths across the logistics and purchasing samples were significant and in the predicted direction. We rely on the SEM analysis in interpreting the results and providing our implications. However, as a test of robustness, we also examined our proposed model using regression analyses, and M A R K E T I N G
S C I E N C E
I N S T I T U T E
found similar results. The SEM and regression results are provided in “Summary Results of Hypothesis Testing.” In conjunction with testing via SEM and regression analyses, we performed a series of competing-model analyses. First, using regression, we examined a competing model in which all knowledge constructs had a direct effect on the 10
outcomes. In analyzing the logistics data, we found the following significant relationships: TOK, QOK, R → Speed; OM, KF, R → Quality; TOK, KF, QOK, R → Cost; and TOK, KF, R, LC → Flexibility. In analyzing the purchasing data, we found that KU, LC → Speed; KF, KI → Quality; KI, LC → Cost; and OM, KU, LC → Flexibility. These results seem to indicate that our model of interrelationships and their depicted effects on outcomes is a more accurate model than one that relates the positive effect of all knowledge constructs to outcomes directly. Second, we examined the modification indices provided in the LISREL analysis. We adopted this approach instead of theorizing competing models because the field of knowledge management is in its infancy. In the logistics analysis, the modification indices suggest that for the knowledge constructs there should be a path between AOK and QOK. In the purchasing analysis, the modification indices suggest that for the knowledge constructs there should be a path between AOK and QOK and between R and LC. Although the AOK-QOK and R-LC paths may be empirically justified, we feel that they are not theoretically justified and therefore we did not add them to the model at this stage of theoretical development.
the flexibility of the process (LC → F) was significant in the logistics sample but not in the purchasing sample. (See “Summary Results of Hypotheses Testing” for differences across the regression analyses.) The paths predicted in H1 through H7b, and in H8a, H8b, H8c, H9a, and H9c were found to be significant, positive, and as expected in the hypotheses, with no statistical difference across groups (χ2∆ < 3.84, df∆ = 1). However, the SEM equivalence analysis revealed that the relationship predicted in H8d (responsiveness to customers’ product needs will positively affect flexibility) was statistically different across groups. Specifically, while the path was significant and positive in both samples, the parameter estimate was significantly higher in the purchasing sample (β10,5 =. 80) as compared with the parameter estimate in the logistics sample (β10,5 = .15), as supported by a χ2∆ = 5.95 (df∆ = 1). Additionally, although the results of the SEM and regression analyses largely correspond, notable differences were found in the purchasing analyses for the following paths: AOK → KU, KU → R, LC → Q , and LC → F.
Discussion and Implications Although we tested our model independently in the two separate samples, we expected similar results in the logistics and purchasing functions. Similar results would indicate that knowledge development can be facilitated across functional boundaries in the supply chain (cf. Maltz and Kohli 1996). On the other hand, differences, or knowledge “asymmetries” (cf. Mishra, Heide, and Cort 1998), would indicate that knowledge development in the supply chain needs to be tied to the functional identification of the professionals involved (Fisher, Maltz, and Jaworski 1997). Our separate SEM analyses of the two samples produced significant differences in only two paths: the effect of learning capacity on quality of the process (LC → Q) was significant in both samples—but positive in the logistics sample and negative in the purchasing sample (contrary to the prediction), and the effect of learning capacity on M A R K E T I N G
S C I E N C E
I N S T I T U T E
In examining the value of knowledge as a strategic resource in marketing functions (cf. Hult and Ketchen 2001), we were guided by two research questions: What are the strategically important knowledge constructs in the marketing functions of logistics and purchasing, and how are they integrated to shape important outcomes in the supply chain? The overall results indicate that the majority of our nine hypotheses and their subsets were supported in both the logistics (n = 545) and purchasing (n = 368) samples (see “Summary Results of Hypothesis Testing”). That is, the set of nine knowledge constructs that we developed and the path-flow model that we created using those constructs were supported by both the logistics and purchasing analyses. We turn now to a discussion of the implications of these findings. 11
Organizational memory For logistics and purchasing functions, it appears that organizational memory drives both accessibility of knowledge and quality of knowledge more effectively than does tacitness of knowledge or knowledge filtering. It appears that the higher the degree of perceived memory that exists in the supply chain, the more accessible the knowledge is for the individuals in the chain (i.e., if you know memory on a supply chain issue exists, then you also know how to access it), and the higher the quality of the knowledge accessed. Second, tacitness of knowledge is the key factor influencing accessibility of knowledge in both samples. Given that tacit knowledge cannot be provided in written format (e.g., in a manual or a report), supply chain members who receive what is perceived as tacit knowledge from another member may also perceive such knowledge flow as accessible. Third, knowledge filtering is the primary determinant of quality of knowledge for both the logistics and the purchasing functions. These results make sense given that knowledge filtering provides the mechanism by which levels of importance are placed on the knowledge obtained. Managerially, these results indicate the importance of organizational memory, tacitness of knowledge, and knowledge filtering for organizations in their quest to develop, implement, and maintain efficient and effective logistics and purchasing operations. However, organizational memory appears to be the most important element of transferability of knowledge; memory should be stressed above tacitness and filtering. Quality of knowledge The results indicate that quality of knowledge is the primary driver of both knowledge use and knowledge intensity. Accessibility of knowledge, although also significant in both samples, assumes a secondary role in affecting the appropriability constructs. Presumably, while large quantities of poor-quality knowledge that is very accessible may affect knowledge use and M A R K E T I N G
S C I E N C E
I N S T I T U T E
intensity positively to some degree, high-quality knowledge always affects knowledge use and intensity more favorably. Intuitively, these results make sense. However, since supply chains are usually comprised of several firms who may not have readily available mechanisms for sharing knowledge, it may be that members often make decisions based on whatever knowledge—high quality or poor quality—is accessible at the time. Our results imply that supply chains should focus on developing mechanisms for sharing highquality knowledge which may not be as readily available. For example, periodical supply chain meetings that make possible face-to-face interaction among corporate buyers and key suppliers may be more valuable to the ultimate performance of the chain than daily e-mails between suppliers and buyers. Knowledge use or knowledge intensity? In the logistics sample, knowledge use had a greater effect on responsiveness than knowledge intensity, while knowledge use and intensity affected learning capacity to the same degree. Thus for logistics, knowledge use had a primary role in the linkage between appropriability and knowledge requirements of production. In other words, logistics places great importance on the use of knowledge to subsequently affect performance via knowledge requirements of production. Managerially, this means that in meeting customers’ product needs, the use of knowledge is more important than the intensity of knowledge inherent in the organization’s logistics practices. In the purchasing sample, knowledge intensity had a greater effect than knowledge use on both responsiveness and learning capacity. Thus, the purchasing function creates greater value in the system by stressing its own purchasing practices than by focusing on the use of knowledge in a particular activity. Managerially, this means that purchasing (inbound portion of the supply chain) creates the greatest value in the system by stressing elements of knowledge intensity while logistics (outbound portion of the supply chain) 12
creates the greatest value by stressing the use of knowledge effectively. Key drivers of performance outcomes The analyses indicate that responsiveness to customers’ product needs was the key driver of success in the purchasing sample. On each performance indicator (i.e., speed, quality, cost, and flexibility), responsiveness had a greater effect than learning capacity in purchasing. The results are almost reverse for the logistics sample: learning capacity had the most influence on the performance indicators of speed, cost, and flexibility. Thus, in the logistics setting, performance is primarily driven by members’ increased skills and knowledge, which is typically reflected in innovative and cutting-edge products. Managerially, this means that logistics professionals should focus on developing a stock of capacity to accomplish tasks. Such “learning capacity” apparently provides logistics professionals with the advanced opportunity to effectively operate within the supply chain. In the purchasing setting, on the other hand, performance is driven by responsiveness to customers’ product needs rather than by the need to provide innovative products developed by the most skilled workers. Managerially, this means that purchasing professionals’ effectiveness is a direct function of the immediate product responsiveness (by the sellers) to the purchasing professionals’ needs (or those of their organization) as opposed to having the “learning capacity” to solve a wide range of issues for the buyers.
Conclusion Given the profound turbulence that characterizes many industries, it seems certain that the requirements of strategic marketing will continue to evolve in unpredictable ways. It will become increasingly important for researchers to build an understanding of the unique resources that facilitate the delivery of products and services. M A R K E T I N G
S C I E N C E
I N S T I T U T E
Likewise, given the importance of effective strategy implementation, building an understanding of knowledge as a strategic resource in supply chains is critical. Using the knowledgebased view, our study has taken a step in that direction by providing empirical evidence that knowledge transferability, capacity for knowledge aggregation, knowledge appropriability, and knowledge requirements of production function together to influence success.
Limitations and Future Research Directions Our results should be interpreted in light of the study’s limitations, which offer guidance for future studies. First, although knowledge has been depicted as a strategic resource (e.g., Wernerfelt 1984), there is no established theory that captures the phenomenon and its intricacies. The knowledge-based view of the firm is the most theoretically sound attempt so far. In our study, we used the knowledge-based view as the theoretical foundation, but augmented it with a broader literature on knowledge to identify nine knowledge constructs tied to four knowledge characteristics (transferability, capacity for aggregation, appropriability, and knowledge requirements of production). A number of other constructs can be identified as important in this context (for example, generation and dissemination of information, as mentioned by Jaworski and Kohli [1993]). Future studies need to take up both fine-grained and coarse-grained research questions regarding knowledge. Such research may uncover omissions and perhaps misrepresentations of the constructs and linkages examined in this study. Second, our focus was on two critical marketing functions within supply chains—logistics and purchasing. Methodologically, we opted to study samples of logistics and purchasing professionals from different organizations. This allowed us to draw conclusions across supply chain functions without the potentially confounding influences of shared firm-level variance. However, broadening the scope to cover knowledge as a strategic 13
resource in other marketing functions (such as product development or consumer behavior) and non-marketing functions (such as operations management or information technology) in supply chains might alter the results. Also, strong support exists for strategic and tactical alignment between functions in an organization (see, for example, Fisher, Maltz, and Jaworski [1997]). However, research that directly addresses alignment issues needs data from multiple nodes and perhaps levels within the same firm (Homburg and Pflesser 2000), and neither the focus of our study nor the data obtained addressed this alignment issue. Our focus was instead on the robustness of the model in explaining knowledge phenomena across marketing functions. Third, we opted to study the path-flow effect of nine knowledge constructs on performance of the process associated with logistics and purchasing. The well-established operations management framework of speed, quality, cost, and flexibility was used to assess performance in this context. However, this framework is also tied to product performance. Future studies need to examine how knowledge-based models affect both process and product performance. Product performance may also be driven by different knowledge constructs than those depicted in the hypothesized model,
Appendix A: Measures and Sources Note: The following items used a 7-point Likert-type scale ranging from “strongly disagree” to “strongly agree.”The word logistics was changed to supply management for the purchasing sample. (We used the term supply management instead of purchasing because the sponsoring organization, which was named the National Association of Purchasing Management, recently changed its name to the Institute of Supply Management.) The symbol * following an item indicates that the item was deleted after the exploratory factor analysis; the symbol ‡ following an item indicates that the item was deleted after the item-level analysis across groups (i.e., because it was not robust across the logistics and purchasing samples). Organizational Memory (adapted from Moorman 1995) We have a great deal of knowledge about logistics. We have a great deal of experience with logistics. We have a great deal of familiarity with logistics. We have invested a great deal of research and development related to logistics. M A R K E T I N G
S C I E N C E
I N S T I T U T E
although this possibility remains theoretically unsubstantiated at this time. Thus, product performance research may also uncover omissions or misrepresentations of the constructs and linkages examined in this study. Finally, we found that our within-construct correlations were for the most part higher than our between-construct correlations for the four process-outcome variables. Future studies should attempt to refine the process-outcome measures used in this study. Despite these limitations, our study offers a significant contribution to the logistics, purchasing, and marketing literature regarding the potential value of knowledge as a strategic resource. ■
Acknowledgements This study was supported by the Council of Logistics Management (CLM), the Institute of Supply Management (ISM), and the Center for International Business Education and Research at Michigan State University. We appreciate the input provided by the Marketing Science Institute, Donald R. Lehmann, John T. Mentzer, David Closs, Phil Carter, and Seyda Deligonul.
Tacitness of Knowledge (items 1 to 4 are adapted from Zander and Kogut 1995; item 5 is new, based on Simonin 1999) reverse coded A useful manual describing our logistics activities can be written for new employees. We have extensive documentation describing our logistics activities for new employees. New personnel can easily learn our logistics activities by talking to skilled workers. ‡ Training new logistics personnel is a quick and easy job. New personnel can easily identify the knowledge needed to perform our logistics activities. Knowledge Filtering (new scale based on Huber 1991 and Moorman 1995) We filter logistics knowledge into levels of importance in our organization. We filter logistics knowledge into levels of importance in our unit. We filter logistics knowledge into levels of importance across units. 14
We filter logistics knowledge into levels of importance for our activities. We filter logistics knowledge into levels of importance to reduce its complexity. We filter logistics knowledge into levels of importance to share it in meaningful ways. We filter logistics knowledge into levels of importance to effectively share it in our organization. Accessibility of Knowledge (based on O’Reilly 1982) Knowledge that exists in our organization is readily available to assist in making our logistics decisions. Logistics knowledge contained in our organization is easily accessible when needed. On the average, it is easy to obtain logistics knowledge from key people in this organization. Quality of Knowledge (adapted from O’Reilly 1982) The logistics knowledge we have is very accurate. The logistics knowledge we have is very reliable. The logistics knowledge we have is very relevant to our needs. The logistics knowledge we have is very specific to our needs.‡ The logistics knowledge we have is exactly what we need. The logistics knowledge we have is very useful. Knowledge Use (adapted from Deshpandé and Zaltman 1982) Our existing knowledge enriched the basic understanding of our latest logistics activity. Our latest logistics activity would have been very different if the existing knowledge had not been available. Our existing knowledge reduced the uncertainty of our latest logistics activity. Our existing knowledge identified aspects of our latest logistics activity that would otherwise have gone unnoticed. We used our existing knowledge to make specific decisions for our latest logistics activity. Without our existing knowledge, our latest logistics decision would have been very different. Knowledge Intensity (adapted from Autio, Sapienze, and Almeida 2000) We have a strong reputation for having cutting-edge knowledge about logistics. Knowledge intensity is a characteristic of our logistics practices. There is a strong knowledge component in our logistics practices. Responsiveness (based on Kohli, Jaworski, and Kumar 1993) We respond effectively to changes in a competitor’s product offerings.‡ We respond rapidly to changes in our customers’ product needs.‡ We periodically review our products to ensure that they are in line with what our customers want. We rapidly attend to product complaints from our customers. M A R K E T I N G
S C I E N C E
I N S T I T U T E
When we find out that our customers are unhappy with a product, we take corrective action immediately. When we find out that our customers would like us to modify a product, we make a concerted effort to do so.‡ Learning Capacity (new scale; item 1 is based on Hurley and Hult 1998; items 2–5 are motivated by Grant 1996a, b) The number of logistics suggestions implemented in our organization is greater than last year.‡ The percentage of skilled logistics workers is greater than last year. The number of logistics individuals learning new skills is greater than last year. The resources spent on learning have resulted in increased logistics productivity. Our learning activities have resulted in better logistics performance than last year. Process Outcomes (The process-outcome framework of speed, quality, cost, and flexibility is based on work in operations management, such as Anderson, Cleveland, and Schroeder 1989; Boyer and Pagell 2000; Hult, Ketchen, and Nichols 2002; McKone, Schroeder, and Cua 2001; Ward et al. 1998; and Youndt et al. 1996.) Speed The length of the order fulfillment process is getting shorter every time. We have seen an improvement in the cycle time of the order fulfillment process recently. We are satisfied with the speediness of the order fulfillment process.* Based on our knowledge of the order fulfillment process, we think it is short and efficient. The length of the order fulfillment process could not be much shorter than today.* Quality The quality of the order fulfillment process is getting better every time. We have seen an improvement in the quality of the order fulfillment process recently. We are satisfied with the quality of the order fulfillment process.* Based on our knowledge of the order fulfillment process, we think it is of high quality. The quality of the order fulfillment process could not be much better than today.* Cost The cost associated with the order fulfillment process is getting better every time. We have seen an improvement in the cost associated with the order fulfillment process recently. We are satisfied with the cost associated with the order fulfillment process.* Based on our knowledge of the order fulfillment process, we think it is cost efficient. The cost associated with the order fulfillment process could not be much better than today.* 15
Flexibility The flexibility of the order fulfillment process is getting better every time. We have seen an improvement in the flexibility of the order fulfillment process recently. We are satisfied with the flexibility of the order fulfillment
process.* Based on our knowledge of the order fulfillment process, we think it is flexible. The flexibility of the order fulfillment process could not be much better than today.*
Appendix B: Measurement Purification Process
Step 3: Equivalence of the Model Structure and Item Loadings across the Samples To test the robustness of the structure of the measurement model and the specific items, we employed a two-step approach. First, we conducted a multisample CFA (using the correlation matrices from the logistics and purchasing samples as independent input into the same multisample model) using LISREL 8.53 ( Jöreskog and Sörbom 1996; Jöreskog et al. 2000). Second, we examined the robustness of each item across the logistics and purchasing samples.
The measurement analysis was accomplished using a fivestep approach: (1) we conducted exploratory factor analyses in each of the logistics and purchasing samples; (2) we tested potential common method variance problems; (3) we tested the robustness of the model structure and each item across the logistics and purchasing samples; (4) we conducted separate CFAs on the logistics and purchasing samples; and (5) we assessed the reliability and validity of the scales. Step 1: Exploratory Factor Analysis Given the relatively large number of 13 constructs and 65 items included in the study and the mix of established and new scales, we first conducted a principal-components factor analysis with varimax rotation using SPSS 11.0. This factor analysis was done for each sample (logistics and purchasing) separately to verify that the expected 13 factors emerged and that the items loaded on their assigned factors in each sample. Both the logistics and purchasing analyses resulted in 13 factors emerging. However, in both samples we found eight items that did not load on the predetermined factor. (Those were S3, S5, Q3, Q5, C3, C5, F3, and F5, which are marked in Appendix A with the symbol *). The 8 items were deleted, leaving 57 items for subsequent analysis. Step 2: Common Method Variance Before moving on to the structure of the general measurement model and the item-level analyses, we conducted an examination of potential common method variance problems. Specifically, since all items used to test the hypotheses in the hypothesized model are based on logistics or purchasing professionals’ subjective judgments, we felt that it was important to establish that common method variance was not likely to be an inhibiting factor in the model testing. Therefore we factor-analyzed the 57 remaining items (with a principal-components extraction and varimax rotation) to see if one single factor would emerge or if one general factor would account for most of the covariance in the variables (e.g., Atuahene-Gima and Ko 2001; Podsakoff and Organ 1986). The items loaded on their conceptually predetermined factor in the logistics analysis and achieved explained variances of 10.9, 10.2, 8.6, 7.4, 6.3, 6.1, 5.8, 5.6, 4.6, 4.1, 2.3, 2.1, and 1.8% for the 13 factors (for a total explained variance of 75.8%). Similarly, the items loaded on their conceptually predetermined factor in the purchasing sample, and achieved explained variances of 12.9, 10.4, 8.1, 7.4, 7.3, 6.7, 6.4, 6.0, 5.5, 2.6, 2.3, 1.8, and 1.6% for the 13 factors (for a total explained variance of 79%). Thus, our inference from this analysis is that common method bias does not appear to be an inhibiting factor in this study. M A R K E T I N G
S C I E N C E
I N S T I T U T E
The model fits were evaluated using a series of indices. The DELTA2 index (Bollen 1989), the relative noncentrality index (RNI; McDonald and Marsh 1990), and the comparative fit index (CFI; Bentler 1990) have been shown to be the most stable fit indices by Gerbing and Anderson (1992). Hu and Bentler (1999) suggested that the Tucker-Lewis index (TLI; Tucker and Lewis 1973) and the root mean square error of approximation index (RMSEA; Steiger and Lind 1980) should be added in evaluating CFA and SEM analyses. Therefore, we used DELTA2, RNI, CFI, TLI, and RMSEA to assess the fit of the CFA model (and later to assess the SEM models). Utilizing these criteria, the two-sample CFA model resulted in a good fit to the data with DELTA2, RNI, CFI, and TLI all being .90, and RMSEA = .11 (χ2 = 22,542.13, df = 3,115) when constraining the parameter estimates (i.e., loadings, factor correlations, and error variances) to be the same across the two samples. Thus, this analysis reveals two solutions, one for the logistics sample and one for the purchasing sample, but the parameter estimates are identical. To test the validity of the constrained model, we allowed the factor loadings to be different across the two samples. Allowing the loadings to be estimated independently from each other in the two samples resulted in similar fit statistics, with DELTA2, RNI, CFI, and TLI all being .90, and RMSEA = .11 (χ2 = 22,398.38, df = 3,057). Using the χ2-difference test suggested by Anderson and Gerbing (1988), we found that the constrained and unconstrained measurement models did in fact differ (χ2∆ = 143.75, df∆ = 58). We therefore proceeded to conduct item-level analysis to identify which items were not robust across the samples. Specifically, after finding general structure-level differences across the samples, we continued by examining the robustness of each item loading across the sample groups. This test involves constraining appropriate pairs of β estimates, one pair at a time, to be equal and different across the two groups, and then evaluating whether the resulting change in the χ2 value is significant with one degree of freedom 16
(Bagozzi and Heatherton 1994; Jöreskog and Sörbom 1996). The results indicate that of the remaining 57 items, 6 items were not equivalent across the logistics and purchasing samples. (Those six, TOK3, QOK4, R1, R2, R6, and KO1, are marked in Appendix A with a ‡ symbol). The 6 items were deleted, leaving 51 items for subsequent analysis. Step 4: Confirmatory Factor Analyses The next step in the analysis of the measurement properties was to conduct separate CFAs of the logistics and purchasing samples. The CFA assessment of the logistics sample resulted in DELTA2, RNI, CFI, and TLI all being .91, and RMSEA = .10 (χ2 = 8,460.16, df = 1,146). The CFA model for the purchasing sample also resulted in a good fit to the data, with DELTA2, RNI, CFI, and TLI all being .91, and RMSEA = .12 (χ2 = 8,337.83, df = 1,146). Thus, the measurement structure of our 13 factors and 51 remaining items produced satisfactory fit statistics for both the logistics and purchasing samples. Therefore, we moved on to test the reliability and validity of the constructs. Step 5: Reliability and Validity Assessments Within the CFA setting, composite reliability was calculated using the procedures outlined by Fornell and Larcker (1981) based on the work of Werts, Linn, and Jöreskog (1974). The formula specifies that:
where CRη = composite reliability for scale η, λγi = standardized loading for scale item γi, and εi = measurement error for scale item γi. The parameter estimates and their associated t-values were also examined, along with the average variance extracted for each construct (Anderson and Gerbing 1988). Average variance extracted was calculated using the following formula:
where Vη = average variance extracted for η, λγi = standardized loading for scale item γi, and εi = measurement error for scale item γi. The composite reliabilities for the 13 scales ranged from .79 to .93 in the logistics sample and from .76 to .96 in the purchasing sample (see “Summary Statistics of Measurement Analysis” for complete reliability results). The factor loadings ranged from .49 to .97 (p < .01) in the logistics sample and from .50 to .96 (p < .01) in the purchasing sample, with average variances extracted ranging from 57.7 to 82.7% (logistics) and from 52.3 to 82.6% (purchasing). Additionally, the 51 purified items were found to be reliable
M A R K E T I N G
S C I E N C E
I N S T I T U T E
and valid when evaluated based on each item’s error variance, modification index, and residual covariation. Also, the skewness and kurtosis results of each item indicated that the data were normally distributed. Discriminant validity was primarily established by calculating the shared variance between pairs of constructs and verifying that it was lower than the average variances extracted for the individual constructs (Fornell and Larcker 1981). Shared variance was calculated as:
γ 2 = 1– ψ , where γ 2 = shared variance between constructs, and with the diagonal element of ψ indicating the amount of unexplained variance. Since η and ε were standardized, γ2 was equal to the squared correlation between the two constructs. In all cases, the average variance extracted was higher than 50%, the recommended cutoff by Fornell and Larcker (1981) (ranging from 52.3% to 82.7%; see “Summary Statistics of Measurement Analysis” for complete results). The shared variances between pairs of all possible scale combinations indicated that the average variances extracted were higher than the associated shared variance in all cases in both samples (see “Correlations Among Variables in Overall Sample”). However, while the scales appear to exhibit discriminant validity according to the test of average variances extracted compared with the corresponding shared variances, we also found that the average within-construct correlations were lower than the between-construct correlations for the paired combination of KU and QOK in the logistics sample. The results are more problematic for the process outcomes of speed, quality, cost, and flexibility—indicating that those four dimensions may in reality be unidimensional. However, the rest of the measurement analysis does not support this unidimensionality. The CFI for a unidimensional model using the logistics data is .79 (χ2 = 1712, df = 53). The CFI for a unidimensional model using the purchasing data is .81 (χ2 = 1251, df = 53). That said, the limitation of the processbased outcome measures has been noted in the limitations section. Finally, we conducted one additional test of discriminant validity following the guidelines of Anderson (1987). This test entailed analyzing all possible pairs of constructs in a series of two-factor CFA models using LISREL. Each model was run twice—once constraining the φ coefficient to unity and once freeing this parameter. A χ2-difference test was then performed on the nested models to assess if the χ2 values were significantly lower for the unconstrained models (Anderson and Gerbing 1988). The critical value (∆χ2(1) > 3.84) was exceeded in all cases. Thus, the 13 constructs and their purified 51 indicators were found to be reliable, valid, and robust across samples. We used these 13 constructs and 51 items in our hypothesis testing.
17
References
Seller Relationships and Customer Firm Costs.” Journal of Marketing 65 ( January), 29–43.
Anderson, James C. (1987), “An Approach for Confirmatory Measurement and Structural Equation Modeling of Organizational Properties.” Management Science 33 (April), 525–41. Anderson, James C., and David W. Gerbing (1988), “Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement.” Journal of Marketing Research 19 (November), 453–60. Anderson, John C., Gary Cleveland, and Roger G. Schroeder (1989), “Operations Strategy: A Literature Review.” Journal of Operations Management 8 (2), 133–58. Atuahene-Gima, Kwaku, and Anthony Ko (2001), “An Empirical Investigation of the Effect of Market Orientation and Entrepreneurship Orientation Alignment on Product Innovation.” Organization Science 12 (1), 54–74. Autio, Erkko, Harry J. Sapienze, and James G. Almeida (2000), “Effects of Age at Entry, Knowledge Intensity, and Imitability on International Growth.” Academy of Management Journal 43 (5), 909–24. Bagozzi, Richard P., and Todd F. Heatherton (1994), “A General Approach to Representing Multifaceted Personality Constructs: Application to Self-Esteem.” Structural Equation Modeling 1 (1), 35–67. Bagozzi, Richard P., and Lynn W. Phillips (1982), “Representing and Testing Organizational Theories: A Holistic Construal.” Administrative Science Quarterly 27 (September), 459–89. Barney, Jay B. (1991), “Firm Resources and Sustained Competitive Advantage.” Journal of Management 17 (1), 99–120. Barney, Jay B. (2001), “Is the Resource-Based ‘View’ a Useful Perspective for Strategic Management Research? Yes.” Academy of Management Review 26 (1), 41–56. Bentler, Peter M. (1990), “Comparative Fit Indexes in Structural Equation Modeling.” Psychological Bulletin 107, 238–46. Bollen, Kenneth A. (1989), Structural Equations with Latent Variables. New York, N.Y.: Wiley & Sons.
Carter, Joseph R., and Ram Narasimhan (1996), “Purchasing and Supply Management: Future Directions and Trends.” International Journal of Purchasing and Materials Management 32 (Fall), 2–12. Cespedes, Frank V. (1996), Managing Marketing Linkages. Upper Saddle River, N.J.: Prentice-Hall. Cohen,Wesley M., and Daniel A. Levinthal (1990),“Absorptive Capacity: A New Perspective on Learning and Innovation.” Administrative Science Quarterly 35 ( January), 128–52. Deligonul, Z. Seyda, and S. Tamer Cavusgil (1997), “Does the Comparative Advantage Theory of Competition Really Replace the Neoclassical Theory of Perfect Competition?” Journal of Marketing 61 (October), 65–73. Deshpandé, Rohit, and Gerald Zaltman (1982), “Factors Affecting the Use of Market Research Information: A Path Analysis.” Journal of Marketing Research 19 (May), 14–31. Farhoomand, Ali F., and Pauline Ng (2000), FedEx Corp.: Structural Transformation Through E-Business. Harvard Business School Case Collection No. HKU098. Fisher, Robert J., Elliot Maltz, and Bernard J. Jaworski (1997), “Enhancing Communication Between Marketing and Engineering:The Moderating Role of Relative Functional Identification.” Journal of Marketing 61 ( July), 54–70. Fornell, Claes, and David F. Larcker (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error.” Journal of Marketing Research 18 (February), 39–50. Gerbing, David W., and James C. Anderson (1992), “Monte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models.” Sociological Methods and Research 21 (November), 132–60. Glazer, Rashi (1991), “Marketing in an InformationIntensive Environment: Strategic Implications of Knowledge as an Asset.” Journal of Marketing 55 (October), 1–19. Glazer, Rashi, and Allen M. Weiss (1993), “Marketing in Turbulent Environments: Decision Processes and the TimeSensitivity of Information.” Journal of Marketing Research 30 (November), 509–21.
Boyer, Kenneth K., and Mark Pagell (2000), “Measurement Issues in Empirical Research: Improving Measures of Operations Strategy and Advanced Manufacturing Technology.” Journal of Operations Management 18 (3), 361–74.
Grant, Robert M. (1996a), “Toward a Knowledge-Based Theory of the Firm.” Strategic Management Journal 17 (Winter)(Special Issue), 109–22.
Brown, John Seely, and Paul Duguid (2001), “Knowledge and Organization: A Social-Practice Perspective.” Organization Science 12 (2), 198–213.
Grant, Robert M. (1996b), “Prospering in DynamicallyCompetitive Environments: Organizational Capability as Knowledge Integration.” Organization Science 7 (4), 375–87.
Cannon, Joseph P., and Christian Homburg (2001), “Buyer-
Grant, Robert M. (1997), “The Knowledge-Based View of
M A R K E T I N G
S C I E N C E
I N S T I T U T E
18
the Firm: Implications for Management Practice.” Long Range Planning 30 (3), 450–4. Hansen, Morten T. (2002), “Knowledge Networks: Explaining Effective Knowledge Sharing in Multiunit Companies.” Organization Science 13 (3), 232–49. Hedlund, Gunnar (1994), “A Model of Knowledge Management and the N-Form Corporation.” Strategic Management Journal 15 (Special Issue), 73–90. Heide, Jan B., and George John (1992), “Do Norms Matter in Marketing Relationships?” Journal of Marketing 56 (April), 32–44. Homburg, Christian, and Christian Pflesser (2000), “A Multiple-Layer Model of Market-Oriented Organizational Culture: Measurement Issues and Performance Outcomes.” Journal of Marketing Research 37 (November), 449–62. Hu, Li-tze, and Peter M. Bentler (1999), “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives.” Structural Equation Modeling 6 (1), 1–55. Huber, George P. (1991), “Organizational Learning: The Contributing Processes and the Literatures.” Organization Science 2 (1), 88–115. Hult, G. Tomas M. (1998), “Managing the International Strategic Sourcing Function as a Market-Driven Organizational Learning System.” Decision Sciences 29 (1), 193–216. Hult, G. Tomas M., Robert F. Hurley, Larry C. Giunipero, and Ernest L. Nichols, Jr. (2000), “Organizational Learning in Global Purchasing: A Model and Test of Internal Users and Corporate Buyers.” Decision Sciences 31 (2), 293–325. Hult, G. Tomas M., and David J. Ketchen, Jr. (2001), “Does Market Orientation Matter? A Test of the Relationship Between Positional Advantage and Performance.” Strategic Management Journal 22 (9), 899–906. Hult, G.Tomas M., David J. Ketchen, Jr., and Ernest L. Nichols, Jr. (2002), “An Examination of Cultural Competitiveness and Order Fulfillment Cycle Time within Supply Chains.” Academy of Management Journal 45 (3), 577–86. Hunt, Shelby D., and Robert M. Morgan (1995), “The Comparative Advantage Theory of Competition.” Journal of Marketing 59 (April), 1–15. Hurley, Robert F., and G. Tomas M. Hult (1998), “Innovation, Market Orientation, and Organizational Learning: An Integration and Empirical Examination.” Journal of Marketing 62 ( July), 42–54. Jaworski, Bernard J., and Ajay K. Kohli (1993), “Market Orientation: Antecedents and Consequences.” Journal of Marketing 52 ( July), 53–70. M A R K E T I N G
S C I E N C E
I N S T I T U T E
Jensen, Michael C., and William H. Meckling (1992), “Specific and General Knowledge and Organization Structure.” In Contract Economics, eds. Lars Werin and Hans Wijkander, 251–74. Oxford: Blackwell. Jöreskog, Karl G., and Dag Sörbom (1996), LISREL 8: User’s Reference Guide. Chicago, Ill.: Scientific Software International. Jöreskog, Karl G., Dag Sörbom, Stephen Du Toit, and Mathilda Du Toit (2000), LISREL 8: New Statistical Features, 2nd ed. Chicago, Ill.: Scientific Software International. Kogut, Bruce, and Udo Zander (1993), “Knowledge of the Firm and the Evolutionary Theory of the Multinational Corporation.” Journal of International Business Studies 24 (4), 625–45. Kohli, Ajay K., Bernard J. Jaworski, and Ajith Kumar (1993), “MARKOR: A Measure of Market Orientation.” Journal of Marketing Research 15 (November), 467–77. Leavitt, Harold J. (1965), “Applied Organizational Change in Industry: Structural, Technological and Humanistic Approaches.” In Handbook of Organizations, ed. James G. March, 1144–70. Chicago, Ill.: Rand McNally. Liebeskind, Julia P. (1996), “Knowledge, Strategy, and the Theory of the Firm.” Strategic Management Journal 17 (Special Issue), 93–107. Mabert, Vincent A., and M. A. Venkataraman (1998), “Special Research Focus on Supply Chain Linkages: Challenges for Design and Management in the 21st Century.” Decision Sciences 29 (3), 537–52. Machlup, Fritz (1980), Knowledge: Its Creation, Distribution, and Economic Significance. Princeton, N.J.: Princeton University Press. Madhavan, Ravindranath, and Rajiv Grover (1998), “From Embedded Knowledge to Embodied Knowledge: New Product Development as Knowledge Management.” Journal of Marketing 62 (April), 1–12. Maltz, Elliot, and Ajay K. Kohli (1996), “Market Intelligence Dissemination across Functional Boundaries.” Journal of Marketing Research 33 (February), 47–61. Marquardt, Michael J., and Angus Reynolds (1994), The Global Learning Organization: Gaining Competitive Advantage through Continuous Learning. Burr Ridge, Ill.: Richard D. Irwin. McDonald, Roderick P., and Herbert W. Marsh (1990), “Choosing a Multivariate Model: Noncentrality and Goodness of Fit.” Psychological Bulletin 107 (2), 247–55. McKone, Kathleen E., Roger G. Schroeder, and Kristy O. Cua (2001), “The Impact of Total Productive Maintenance 19
Practices on Manufacturing Performance.” Journal of Operations Management 19 (1), 39–58. Menon, Anil, and P. Rajan Varadarajan (1992), “A Model of Marketing Knowledge Use Within Firms.” Journal of Marketing 56 (October), 53–71. Mentzer, John T., Daniel J. Flint, and G. Tomas M. Hult (2001), “Logistics Service Quality as a Segment-Customized Process.” Journal of Marketing 65 (October), 82–104. Mishra, Debi Prasad, Jan B. Heide, and Stanton G. Cort (1998), “Information Asymmetry and Levels of Agency Relationships.” Journal of Marketing Research 35 (August), 277–95. Mohr, Jakki J., Robert J. Fisher, and John R. Nevin (1996), “Collaborative Communication in Interfirm Relationships: Moderating Effects of Integration and Control.” Journal of Marketing 60 ( July), 103–15. Montgomery, David B., and Charles B. Weinberg (1979), “Toward Strategic Intelligence Systems.” Journal of Marketing 43 (Fall), 41–52. Moorman, Christine (1995), “Organizational Market Information Processes: Cultural Antecedents and New Product Outcomes.” Journal of Marketing Research 32 (August), 318–35. Niraj, Rakesh, Mahendra Gupta, and Chakravarthi Narasimhan (2001), “Customer Profitability in a Supply Chain.” Journal of Marketing 65 ( July), 1–16. Nonaka, Ikujiro (1994), “A Dynamic Theory of Organizational Knowledge Creation.” Organization Science 5 (1), 14–37. O’Reilly, Charles A., III (1982), “Variations in Decision Makers’ Use of Information Sources: The Impact of Quality and Accessibility of Information.” Academy of Management Journal 25 (4), 756–71. Penrose, Edith T. (1959), The Theory of the Growth of the Firm. New York, N.Y.: Wiley. Podsakoff, Philip M., and Dennis W. Organ (1986), “SelfReports in Organizational Research: Problems and Prospects.” Journal of Management 12 (4), 531–44. Rindfleisch, Aric, and Christine Moorman (2001), “The Acquisition and Utilization of Information in New Product Alliances.” Journal of Marketing 65 (April), 1–18.
Simon, Herbert A. (1991), “Bounded Rationality and Organizational Learning.” Organization Science 2 (February), 125–34. Simonin, Bernard (1999), “Ambiguity and the Process of Knowledge Transfer in Strategic Alliances.” Strategic Management Journal 20 (7), 595–623. Sinkula, James M. (1994), “Market Information Processing and Organizational Learning.” Journal of Marketing 58 ( January), 35–45. Slater, Stanley F., and John C. Narver (1995), “Market Orientation and the Learning Organization.” Journal of Marketing 59 ( July), 63–74. Spender, J-C. (1996), “Making Knowledge the Basis of a Dynamic Theory of the Firm,” Strategic Management Journal 17 (Special Issue), 45–62. Steiger, J. H., and J. C. Lind (1980), “Statistically Based Tests for the Number of Common Factors.” Presented at the annual meeting of the Psychometric Society, Iowa City, Iowa. Tucker, L. R., and C. Lewis (1973), “A Reliability Coefficient for Maximum Likelihood Factor Analysis.” Psychometrika 38, 1–10. Venkatesh, R., Ajay K. Kohli, and Gerald Zaltman (1995), “Influence Strategies in Buying Centers.” Journal of Marketing 59 (October), 71–82. Ward, Peter T., John K. McCreery, Larry P. Ritzman, and Deven Sharma (1998), “Competitive Priorities in Operations Management.” Decision Sciences 29 (4), 1035–46. Wernerfelt, Birger (1984), “A Resource-Based View of the Firm.” Strategic Management Journal 5, 171–80. Werts, C. E., R. I. Linn, and Karl G. Jöreskog (1974), “Interclass Reliability Estimates: Testing Structural Assumptions.” Educational and Psychological Measurement 34, 25–33. Youndt, Mark A., Scott A. Snell, James W. Dean, and David P. Lepak (1996), “Human Resource Management, Manufacturing Strategy, and Firm Performance.” Academy of Management Journal 39 (4), 836–66. Zander, Udo, and Bruce Kogut (1995), “Knowledge and the Speed of the Transfer and Imitation of Organizational Capabilities: An Empirical Test.” Organization Science 6 (1), 76–92.
Simon, Herbert A. (1957), Administrative Behavior. New York, N.Y.: MacMillan. Report No. 03–100 “Knowledge as a Strategic Resource in Logistics and Purchasing” © 2003 Tomas M. Hult, S. Tamer Cavusgil, and Roger J. Calantone; Report Summary © 2003 Marketing Science Institute M A R K E T I N G
S C I E N C E
I N S T I T U T E
20
