effects of logistics capabilities on firm performance: Customer-focused versus information-focused capabilities, The

Zhao, Meng

Competitive advantage may be gained from two main sources: assets and the capabilities that enable assets to be deployed advantageously (Dierickx and Cool 1989). Day (1994, p.38) defined capabilities as "complex bundles of skills and accumulated knowledge, exercised through organizational processes, which enable firms to coordinate activities and make use of their assets." Management's task is to exploit and leverage firm specific assets and capabilities (Mahoney and Pandian 1992).

This research focuses on the relationships to performance of two types of capabilities: customerfocused capabilities and information-focused capabilities. The connection between customerfocused capabilities and firm performance has long been recognized in marketing literature (e.g., Day 1994). In the logistics field, increased attention has been given to the relationships among customer-focused logistics capabilities and firm performance (Innis and La Londe 1994; Novack, Langley, and Rinehart 1995; Stank and Lackey 1997). In particular, Bowersox, Closs, and Stank (1999) incorporated key customer-focused capabilities into a "customer integration" construct that formed one core competence of firms achieving high levels of supply chain logistical integration.

Information technology and information-focused capabilities have increasingly been viewed as key predictors of firm performance. For example, EDI (electronic data interchange), e-commerce, e-logistics, ERP (enterprise resource planning), data warehousing, and data mining are current buzzwords in the business community. In logistics, the importance of information technology has long been recognized (e.g., Fawcett, Calantone, and Smith 1996; Williams et al. 1997). Bowersox, Closs, and Stank positioned information-focused capabilities as important enablers of improved firm performance in best practice firms.

The first goal of this research is to propose and test a model of the relationships among customerfocused capabilities, information-focused capabilities, and firm performance. We begin by grounding each independent construct in prior research in marketing, IT, and logistics, and then develop hypotheses from this review. A methodology for testing the hypothesized model is described and followed by a discussion of results.

BACKGROUND

Customer-Focused Capabilities

Various research streams in marketing and logistics support the notion that firms have to develop customer-focused capabilities in order to achieve superior performance. The concept of "market orientation" represents superior skills in understanding and satisfying customers. Empirical evidence supports the proposition that market orientation is positively associated with superior performance (Deshpande, Farley, and Webster 1993; Hunt and Morgan 1995; Jaworski and Kohli 1993; Narver and Slater 1990). Day argued that organizations could become more market oriented by identifying and building the special internal and external capabilities that set marketdriven organizations apart. In particular, market-driven organizations have superior customer emphasis capabilities, including market sensing, customer linking, and channel bonding.

Research in relationship marketing also emphasizes the importance of customer-focused capabilities (Gummesson 1988; Jackson 1985). Morgan and Hunt (1994, p. 22) defined relationship marketing as "all marketing activities directed toward establishing, developing and maintaining successful relational exchanges." They further argued that successful relationship marketing requires relationship commitment and trust. From a relationship marketing perspective, customer-focused capabilities that create and manage customer commitment and trust are required to develop and maintain long-term relationships with customers.

In logistics, Bowersox, Closs, and Stank (1999) provide a comprehensive conceptualization of customer-focused capabilities, calling it "customer integration." Customer integration is "the competency of building lasting distinctiveness with customers of choice" (p. 42) and involves "identifying the long-term requirements, expectations, and preferences of current and/or potential customers and markets, and focusing on creating customer value" (p. 31). They propose four capabilities within the customer integration concept-segmental focus, relevancy, responsiveness, and flexibility. Segmental focus reflects "the belief that firms should identify core customers best suited to be their business clients and then meet or exceed expectations by providing unique value-added service" (p. 41); relevancy requires "firms to satisfy not only existing needs but also those that may emerge" (p. 41); responsiveness is "the accommodation of unique and/or unplanned customer requirements" (p. 42); and flexibility is "adaptation to unexpected operational circumstance" (p. 42). They found that, overall, firms with high customer integration scores were better performers. Other researchers (e.g., Emerson and Grimm 1998; Morash, Droge and Vickery 1996; Stank and Lackey 1997) have also found positive relationships between perceptions of performance and flexibility, responsiveness, and customer expectations. Thus:

H1: Customer-focused capabilities (comprising segmental focus, relevancy, responsiveness, and flexibility) are positively linked to firm performance.

Information-Focused Capabilities

Information technology may be defined as "any form of computer-based information system, including mainframe as well as microcomputer applications" (Orlikowski and Gash 1992, p. 2). Until recently, researchers seemed to agree that information technology was directly linked with firm performance and sustainable competitive advantage (e.g., McFarland 1984; Parsons 1983; Wiseman 1985). It was also argued that information technology had the potential to affect a full range of strategic and industry variables, such as cost positions, economies of scale, and power relations with buyers and suppliers (Benjamin et al. 1984; Cash and Konsynski 1985; Clemons 1986; Porter 1985).

Recent research, however, has challenged these arguments. Zahra and Covin (1993) found no direct technology-performance connection. Floyd and Wooldridge (1990) found no overall connection between ATM adoption and performance. Clemons and Row (1991) portrayed information technology as a commodity, being neither difficult to transfer nor to imitate. Resource-based theory, therefore, predicts that competitive imitation eventually erodes most information technology based advantages. Powell and Dent-Micallef concur: "firms that do not adopt (information technology) will have higher cost structures and therefore disadvantage" (1997, p. 378). They further argue that successfully leveraging information technology requires complementary human resources (e.g., organization, open communication, consensus, and CEO commitment) and business resources (e.g., supplier relationships, supplier information technology, process redesign, and teams).

The impact of information technology on logistics performance has frequently been the subject of research in logistics (e.g., Bowersox, Closs, and Stank 1999; Fawcett, Calantone, and Smith 1996; Global Logistics Research Team at Michigan State University 1995; Gustin, Daugherty, and Stank 1995; Williams et al. 1997). Research by the Global Logistics Research Team at Michigan State University portrayed three dimensions of information-focused capabilities: information technology, information sharing, and connectivity. Information technology was defined as "the hardware, software, and network investment and design to facilitate processing and exchange" (p. 137); information sharing as "the willingness to exchange key technical, financial, operational, and strategic data" (p. 151); and connectivity as "the capability to exchange data in a timely, responsive, and usable format" (p. 160).

Logistics research has consistently shown that managing information technology is a competence of critical importance to logistics performance. Little research, however, provides a holistic view of information technology or addresses its relationship with other logistics capabilities. Numerous studies have revealed a positive association between EDI-one type of information technology that is critically important in logistics-and anticipated efficiency and service advantage (e.g., O'Callaghan, Kaufmann, and Konsynski 1992). EDI has also been associated with shorter cycle time (Sutton 1997); inbound shipment quality (Walton and Marucheck 1997); overall perceived value (Williams, Magee, and Suzuki 1998); and lower costs (bearing 1990; Sutton 1997).

Both resource-based theory and logistics research support the contention that information-- focused capabilities directly impact firm performance. By combining IT and the human factors of IT application, information-focused capabilities as a set represent firm specific skills, expertise, and processes which are difficult to move and imitate. Thus:

H2: Information-focused capabilities (comprising information technology, information sharing, and connectivity) are positively linked to firm performance.

Customer-Focused Versus Information-Focused Capabilities

The knowledge management literature has investigated the relationship between customer-- and information-focused capabilities. One stream of research portrays the prime role of the firm as integrating existing knowledge that is specialized among employees (Grant 1996). Firm capabilities and the competitive advantages derived from them, therefore, depend on the firm's ability to access and integrate specialized knowledge. Information-focused capabilities provide the means for the firm to build efficient mechanisms for integration of customer-focused knowledge, while customerfocused capabilities provide the specialized knowledge itself.

Other research focuses on the transfer and diffusion of knowledge and learning within organizations (e.g., Boisot 1995; Levitt and March 1988). This research portrays knowledge in two categories: tacit knowledge which is "difficult to articulate in a way that is meaningful and complete" and codified knowledge (Teece 1998, p. 63). Kogut and Zander (1992) noted that codified knowledge could be revealed by communication. Hansen, Nohria, and Tierney (1999) found that those firms that focused on the transfer of codified knowledge invested heavily in information technology. These studies show that information-focused capabilities provide the means to transfer knowledge more effectively and efficiently within the organization. Customer-focused capabilities, on the other hand, develop intimate knowledge of customers and hence make codification more likely, thus enhancing information-focused capabilities.

To the extent that customer-focused capabilities are knowledge driven, development of such capabilities will demand the parallel development of information-focused capabilities. Equally, firms emphasizing information-focused capabilities will soon have knowledge of the value of customer-focused capabilities (e.g., through benchmarking), making development of the latter more likely. In this way, the development of one of the two types of capabilities is connected to the development of the other. Thus:

H3: Information-focused capabilities and customer-focused capabilities are positively related to one another.

The relationships described above are portrayed in Figure 1.

METHOD

The sample design and measures used to conduct the research are described in the following sections.

Sample Design

A survey population was selected from the Council of Logistics Management (CLM) membership listing. Given the strategic focus of the research, it was decided to mail a questionnaire to the senior logistics or supply chain executive in each North American-based (i.e., Canada, Mexico, and the United States) manufacturing, wholesale/distributing, and retail firm. These executives were chosen as key informants due to their frequent interactions with key customers and supply chain partners. Additionally, executive compensation and promotions are highly dependent on reaching established logistics service goals. Consequently, these executives are actively involved in tracking, understanding, and assessing the logistical service achieved by the firm compared to their competitors. Such competence and awareness suggests that the sample's perceived evaluations are reasonably credible.

Each firm or SBU received only one questionnaire. The total sampling frame included managers from 2,680 firms; 306 fully validated usable responses were received. Respondent positions ranged from CEO/Presidents to Directors/Managers. Respondents' firms represented ten different groupings. Table 1 summarizes the respondent information.

The 11.5% response rate is related to the length and comprehensive nature of the questionnaire, as well as the confidential nature of the information being requested. While anonymity was guaranteed, some executives may have doubted such claims. Additionally, the response rate was probably influenced by the decision to seek only responses from the most senior executives. Senior executives have the least amount of free time available and are typically inundated with requests to respond to surveys.

From a statistical standpoint, the primary concern related to low response rates is the possibility that the sample data are non-random, i.e., do not represent the general population. Non-- randomness could create bias and inconsistency of the parameter estimates, thus resulting in invalid conclusions regarding the data. If the data can be shown to be random, however, the parameter estimates are assumed to be unbiased and consistent. The only pitfall of missing data, therefore, would be lowered statistical efficiency due to reduced sample size. An analysis to assess the presence of nonresponse bias was conducted using procedures recommended by Armstrong and Overton (1977). The procedure requires responses to be numbered sequentially in the chronological order in which they are received. Next, mean scores provided by the first quartile of responses are compared to those provided by the last quartile. The first quartile of responses is assumed to represent respondents who are most motivated to participate; the last quartile is assumed to be most similar to non-respondents since their replies took the longest time and most effort to obtain. No significant differences in means between the groups indicate that there is no evidence to suggest non-response bias. In the current research, comparison of means of 31 relevant variables across groups revealed no significant differences (at p

Missing values were treated by listwise deletion. Although this loses more data than pairwise deletion and other missing value replacement methods, only listwise deletion leads to consistent estimation in most structural equation models (Bollen 1989). The effective sample size was reduced to 195. Univariate skewness and kurtosis were examined, with no evidence of serious violations of univariate normality detected. Multivariate normality, examined through Martha's Coefficients and Normalized Estimates (Mardia 1970, 1974), was supported.

Measurement

Measures used to assess customer-focused and information-focused capabilities were derived from previous research. Each customer- and information-focused capability was measured by four individual items using five-point Likert scales. The arithmetic mean of the four items was used to create a value for the individual dimension. Means and reliabilities for each item are shown in Table 1 with Cronbach's alphas ranging from .58 to .73. This range is consistent with the suggested standard of approximately .60 and above (Nunnally 1978). As such, the authors determined that all items should be retained to remain consistent with the operationalizations of the constructs used in previous research (Bowersox, Closs, and Stank 1999). Additionally, further analysis using CFA confirmed the statistical acceptability of the scales.

Anderson and Oliver (1987) classified performance measures into those focusing on the final outcome-based performance versus behavior-based performance, and Haytko (1994) summarized their respective shortfalls. A performance construct that incorporates both types of measures is advisable (Anderson and Oliver 1987; Haytko 1994). Customer satisfaction and return on assets (ROA) are common behavior and outcome-based measures used in evaluating firm performance in the marketing and management literatures, including logistics (Anderson and Oliver 1987). Logistics cost is also an outcome-based measure used quite often when evaluating logistics efficiency (Bowersox, Closs, and Stank 1999; MSU Global Logistics Research Team 1995).

In the current research, both outcome-based measures (ROA, and logistics cost) as well as a behavior-based measure (customer satisfaction) are used to develop a balanced assessment of firm performance. Respondents were asked to rate their firms' performance in each of these areas in comparison with their strongest competitors, using five-point Likert scales. We chose the benefits of using diverse items over those of high reliability in order to present a broad performance construct consistent in level of analysis with the independent constructs. Means and reliabilities for the performance scale items are also presented in Table 2.

DATA ANALYSIS AND RESULTS

Construct Validity

To assess overall measurement validity, a confirmatory analysis (CFA) was conducted using EQS 3.5 (Bentler 1989). Following common practice, three goodness-of-fit measures were employed, one being the chi-square test (Hu and Bentley 1995). The comparative fit index (CFI) was also used as CFI is less sensitive to sample size and model specification than chi-square (Bentley 1990). The third was root mean squared error of approximation (RMSEA), which assesses model discrepancy per degree of freedom. RMSEA values less than or equal to .08 are considered reasonable (Browne and Cudeck 1993).

The overall fit was satisfactory: chi-square = 49.036, df = 32, p = .028; CFI = .980; RMSEA = .052. All measurement parameters were significant (p

Structural Equation Model Analysis

To test our proposed model (Figure 1), Structural Equations Modeling (SEM) was used to estimate the relationships among customer-focused capabilities, information-based capabilities, and firm performance. The goodness-of-fit for the model was satisfactory: chi square = 49.036, df= 32, p = .023; CFI = .980; RMSEA =.052. The standardized estimates and t-values are shown in Table 3. The standardized coefficient between customer-focused capabilities and firm performance was .558 (pc.01), supporting HI. The standardized coefficient between information-based capabilities and firm performance was. 104 (p>. 10). Therefore, H2 was not supported. Standardized estimates of the link between customer-focused capabilities and information-focused capabilities was .787 (p

IMPLICATIONS AND CONCLUSION

The results of this research provide important empirical evidence that enhances understanding of the theoretical relationships among customer-focused capabilities, information-focused capabilities, and firm performance. Figure 2 summarizes the findings.

The research findings reveal that customer-focused capabilities were significantly related to firm performance. This implies that firms should promote closeness and commitment in processes that link them with their customers. The results revealed no direct link between information-focused capabilities and performance. This was an interesting finding that, on the surface, refutes firms' investments in information technology. This implication, however, ignores the bigger picture. Information-focused capabilities did influence customer-focused capabilities, which in turn were shown to improve performance. Therefore, best practice firms should focus on both customer- and information-focused capabilities.

Finding customer-focused capabilities to be significantly related to firm performance is consistent with past research. Consistent success is dependent on a firm's ability to create value for end-customers. Firms that can position themselves through customized capabilities understand that sustainable advantage comes from the ability to tailor product/service offerings to the needs of each major customer or customer segment. While sound operations are a prerequisite of such an approach, these firms go beyond the basics by developing a meaningful understanding of customers' needs supported by flexible processes that enable them to create customized solutions. This type of approach can progress to an intimacy with customer operations and a proactive search for customer closeness. Firms share information and use it to be more responsive to customers. Ultimately, this can lead to partnering and alliance formation to solidify the relationship and sustain differential advantage.

Customer-focused capabilities enable firms to build lasting distinctiveness with customers of choice. This requires firms to assess their own strengths and weaknesses in service capability relative to the needs and desires of top customers. Since few firms can fully satisfy every customer or market segment, leading firms are increasingly using such assessment to select where and where not to compete. Top firms recognize the differences in the needs and desires of major customers and design offerings according to those needs.

The research findings reveal that information-focused capabilities alone cannot be considered a distinctive factor directly relating firm performance. Information-focused capabilities must be used to facilitate the creation of other specific, difficult to imitate capabilities. The impact of information technology is not manifested through hardware or through a particular application such as EDI; rather, information technology must be leveraged through sharing and connectivity across departments and the supply chain. The resulting information-focused capabilities can significantly enhance other firm capabilities as demonstrated by its significant relationship with customer-focused capabilities in the current research.

The results help to explain why many firms fail to improve performance after expending considerable resources to adopt new information technology while other firms succeed. In a case study of IT strategies comparing Japanese and Western firms, Bensaou and Earl (1998) observed that "Western bias is toward technology for technology's sake; the Japanese bias is toward appropriate technology" (p. 124). Our results imply that firms should use their information-focused capabilities to support and facilitate customer-focused capabilities, which in turn are related to improved performance. Investing and developing information-focused capabilities cannot be justified directly by performance goals, but rather by the goal of building unique customer-focused capabilities. Specifically, information-focused capabilities can integrate and diffuse knowledge related to segmental focus, relevancy, responsiveness, and flexibility.

Future research should investigate new operationalizations of the constructs. Both customer-- and information-focused capabilities were measured by the mean of four measurement items which assumes that the items are equally weighted in constructing each capability. Although this is adequate given our purpose of examining macro-level constructs, a more detailed analysis of the factor structure of items to capability is warranted. Such a second order factor structure analysis would be revealing for both theory-building and managerial purposes. An additional area for future research is determining whether other capabilities exhibit the same pattern of direct and indirect relationships with performance as those demonstrated by information-focused capabilities.

NOTES

Anderson, Erin and Richard L. Oliver (1987), "Perspectives on Behavior-Based Versus OutcomeBased Salesforce Control Systems," Journal of Marketing, Vol. 51, No. 4, pp. 76-88.

Armstrong, J. Scott and Terry S. Overton (1977), "Estimating Non-Response Bias in Mail Surveys," Journal of Marketing Research, Vol. 14, No. 3, pp. 396-402.

Benjamin, Robert L, John R. Rockart, Michael S. Scott Morton, and John Wyman (1984), "Information Technology: A Strategic Opportunity," Sloan Management Review, Vol. 25, No. 3, pp. 3-10.

Bensaou, M. and Michael Earl (1998), "The Right Mind-Set for Managing Information Technology," Harvard Business Review, Vol. 76, No. 5, pp. 118-128.

Bentley, Peter M. (1989), EQS Structural Equations Program Manual, Los Angeles, CA: BMDP Statistical Software, Inc.

Bentley, Peter M. (1990), "Comparative Fit Indexes in Structural Models," Psychological Bulletin, Vol. 107, No. 2, pp. 208-246.

Bohn, Rogber E. (1994), "Measuring and Managing Technological Knowledge," Sloan Management Review, Vol. 36, No. 1, pp. 61-73.

Boisot, Max H. (1995), "Is Your Firm a Creative Destroyer? Competitive Learning and Knowledge Flows in the Technological Strategies of Firms," Research Policy, Vol. 24, No. 4, pp. 489-506.

Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York: John Wiley & Sons.

Bowersox, Donald J., David J. Closs, and Theodore P. Stank (1999), 211 Century Logistics: Making Supply Chain Integration A Reality, Oak Brook, EL: Council of Logistics Management.

Browne, Michael W. and Robert Cudeck (1993), "Alternative Ways of Assessing Model Fit," in Testing Structural Equation Models, Kenneth A. Bollen and J. Scott Long eds., Newbury Park, CA: SAGE Publications, pp. 136-162.

Cash, James I. Jr. and Benn R. Konsynski (1985), "IS Redraws Competitive Boundaries," Harvard Business Review, Vol. 63, No. 2, pp. 134-142.

Clemons, Eric K. (1986), "Information Systems for Sustainable Competitive Advantage," Information & Management, Vol. 11, No. 3, pp. 131-136.

Clemons, Eric K. and Michael C. Row (1991), "Sustaining IT Advantage: The Role of Structural Differences," MIS Quarterly, Vol. 15, No. 3, pp. 275-292.

Day, George S. (1994), "The Capabilities of Market-Driven Organizations," Journal of Marketing, Vol. 58, No. 4, pp. 37-52.

bearing, Brian (1990), "The Strategic Benefits of EDI," The Journal of Business Strategy, Vol. 11, No. 1, pp. 4-6.

Deshpande, Rohit, John U. Farley, and Frederick Webster, Jr. (1993), "Corporate Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad Analysis,"Journal of Marketing, Vol. 57, No. 1, pp. 23-37.

Dierickx, Ingemar and Karel Cool (1989), "Asset Stock Accumulation and Sustainability of Competitive Advantage," Management Science, Vol. 35, No. 12, pp. 1504-1511.

Emerson Carol J., and Curtis M. Grimm (1998), "The Relative Importance of Logistics and Marketing Customer Service: A Strategic Perspective," Journal of Business Logistics, Vol. 19, No. 1, pp. 17-32.

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.

Floyd, Steven W. and Bill Wooldridge (1990), "Path Analysis of the Relationship between Competitive Strategy, Information Technology, and Financial Performance," Journal of Management Information Systems, Vol. 7, No. 1, pp. 47-64.

Glazer, Rashi (1991), "Marketing in an Information-Intensive Environment: Strategic Implications of Knowledge as an Asset," Journal of Marketing, V. 55, No. 3, pp. 1-19.

Global Logistics Research Team at Michigan State University (1995), World Class Logistics: The Challenge of Managing Change, Oak Brook, IL: Council of Logistics Management.

Grant, Robert M. (1996), "Toward a Knowledge-Based Theory of the Firm," Strategic Management Journal, Vol. 17, Winter Special Issue, pp. 109-122.

Gummesson, Evert (1998), "Implementation Requires A Relationship Marketing Paradigm," Journal of the Academy of Marketing Science, Vol. 26, No. 3, pp. 242-249.

Gustin, Craig M., Patricia J. Daugherty, and Theodore P. Stank (1995), "The Effects of Information Availability on Logistics Integration," Journal of Business Logistics, Vol. 16, No. 1, vi). 1-21.

Hair, Joseph F. Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black (1998), Multivariate Data Analysis, 5th Ed. Upper Saddle River, NJ: Prentice Hall.

Hansen, Morten T., Nitin Nohria, and Thomas Tierney (1999), "What's Your Strategy for Managing Knowledge?" Harvard Business Review, Vol. 77, No. 2, pp. 106-116.

Haytko, Diana L. (1994), "The Performance Construct in Channels of Distribution: A Review and Synthesis," in American Marketing Association Proceedings: Marketing Theory and Applications (Winter), C. W. Park and D. C. Smith eds., pp. 262-271

Hu, Li-tze and Peter M. Bender (1995), "Evaluating Model Fit," in Structural Equation Modeling: Concepts, Issues, and Applications, R. H. Hoyle ed., Thousand Oaks, CA: Sage Publications.

Hunt, Shelby D. and Robert M. Morgan (1995), "The Comparative Advantage Theory of Competition," Journal of Marketing, Vol. 59, No. 2, pp. 1-15.

Innis, Daniel E. and Bernard J. La Londe (1994), "Customer Service: The Key to Customer Satisfaction, Customer Loyalty, and Market Share," Journal of Business Logistics Vol. 15, No. 1, pp. 1-28.

Jackson, Barbara Bund (1985), Winning and Keeping Industrial Customers, Lexington, KY Lexington Books.

Jaworski, Bernard and Ajay Kohli (1993), "Market Orientation: Antecedents and Consequences," Journal of Marketing, Vol. 57, No. 3, pp. 53-70.

Kogut, Bruce and Udo Zander (1992), "Knowledge of the Finn, Combinative Capabilities, and the Replication of Technology," Organization Science, Vol. 3, No. 3, pp. 383-397.

Levitt, Barbara and James G. March (1988), "Organizational Learning," Annual Review of Sociology, Vol. 14, pp. 319-340.

Mahoney, John T. and J. Rajendran Pandian (1992), "The Resource-Based View Within the Conversation of Strategic Management," Strategic Management Journal, Vol. 13, No. 6, pp. 363-380.

Mardia, K. V. (1970), "Measures of Multivariate Skewness and Kurtosis with Applications," Biometrika, Vol. 57, pp. 519-530.

Martha, K. V. (1974), "Applications of Some Measures of Multivariate Skewness and Kurtosis in Testing Normality and Robustness Studies," Sankhva, Vol. B, No. 36, pp. 115-128.

McFarland, F. Warren (1984), "Information Technology Changes the Way You Compete," Harvard Business Review, Vol. 62, No. 3, pp. 98-103.

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.

Morash, Edward A., Cornelia L. M. Droge, 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.

Narver, John C. and Stanley F. Slater (1990), "The Effect of a Marketing Orientation on Business Profitability," Journal ofMarketing, Vol. 54, No. 4, pp. 20-35.

Nunnally, Jim C. (1978), Psychometric Theory, 21 Ed. New York: McGraw-Hill

Novack RobertA., C. John Langley, Jr., and Lloyd M. Rinehart (1995), Creating Logistics Value: Themes for the Future, Oak Brook, IL: Council of Logistics Management.

O'Callaghan, Ramon, Patrick J. Kaufmann, and Benn R. Konsynski (1992) "Adoption Correlates and Share Effects of Electronic Data Interchange Systems in Marketing Channels," Journal of Marketing, Vol. 56, No. 2, pp. 45-56.

Orlikowski, Wanda J. and Debra C. Gash (1992), "Changing Frames: Understanding Technological Change in Organizations," Center for Information Systems Research, Working Paper, Massachusetts Institute of Technology.

Parsons, Gregory L. (1983), "Information Technology: A New Competitive Weapon," Sloan Management Review, Vol. 25, No. 1, pp. 3-12.

Porter, Michael (1985), Competitive Advantage, New York: Free Press.

Powell, Thomas C. and Anne Dent-Micallef (1997), "Information Technology as Competitive Advantage: The Role of Human, Business, and Technology Resources," Strategic Management Journal, Vol. 18, No. 5, pp. 375-405.

Stank, Theodore P. and Charles W. Lackey, Jr. (1997), "Enhancing Performance Through Logistical Capabilities in Mexican Maquiladora Firms," Journal of Business Logistics, Vol. 18, No. 1, pp. 91-123.

Sutton, Mathias J. (1997); "The Role of Electronic Data Interchange in the Transportation Industry: Part 1," Defense Transportation Journal, Vol. 53, No. 4, pp. 10-12.

Teece, David J. (1998), "Capturing Value from Knowledge Assets: The New Economy, Markets for Know-How, and Intangible Assets," California Management Review, Vol. 40, No. 3, pp. 55-79.

Walton, Steve V. and Ann S. Marucheck (1997), "The Relationship between EDI and Supplier Reliability," The International Journal of Purchasing and Materials Management, Vol. 33, No. 3, pp. 33-35.

Williams, Lisa R., George D. Magee, and Yoshinori Suzuki (1998), "A Multidimensional View of EDI: Testing the Value of EDI Participation to Firms," Journal of Business Logistics, Vol. 19, No. 2, pp. 73-87.

Williams, Lisa R., Avril Nibbs, Dimples Irby, and Terence Finley (1997), "Logistics Integration: The Effect of Information Technology, Team Composition, and Corporate Competitive Positioning," Journal ofBusiness Logistics, Vol. 18, No. 2, pp. 31-41.

Wiseman, Charles (1985), "Strategic Vision," Computerworld, May 20, pp. 1-17.

Zahra, Shaker A. and Jeffrey G. Covin (1993), "Business Strategy, Technology Policy, and Firm Performance," Strategic Management Journal, Vol. 14, No. 6, pp. 451-478.

Meng Zhao

Cornelia Droge

and

Theodore P. Stank

Michigan State University

ABOUT THE AUTHORS

Meng Zhao is a Ph.D. student of Marketing and Supply Chain Management at Michigan State University. His research interests include marketing and logistics management strategies and modeling.

Cornelia Droge is Professor, Department of Marketing and Supply Chain Management, Michigan State University. Her research interests are in the areas of marketing and logistics management and strategy. She has published in the Journal of Business Logistics, Journal of Marketing Research, Administrative Science Quarterly, and other journals. She is co-author of three books.

Theodore P. Stank (Ph.D. The University of Georgia) is Associate Professor of Logistics and Supply Chain Management at Michigan State University. He is co-author of 21st Century Logistics, Making Supply Chain Integration a Reality and has over 40 articles in the areas of logistics strategy, customer relevance, and internal and external integration in such journals as Business Horizons, Journal of Business Logistics, Journal of Operations Management, Supply Chain Management Review, and Transportation Journal.

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