Strategic buyer-supplier relationships, information technology and external logistics integration.
Supply chain management (SCM) seeks to improve performance through elimination of waste and more efficient use of internal and external supplier capabilities and technology, creating a seamlessly coordinated supply chain and thus elevating interfirm competition to inter-supply chain competition (Anderson and Katz 1998). Many firms have recently embraced the notion of strategic buyer-supplier relationships to (1) improve efficiency and effectiveness across the value chain and (2) seamlessly integrate their physical distribution function with supply partners to achieve greater benefits.
Logistics management is the part of SCM that plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers' requirements (CSCMP 2006). With the increasing globalization of markets in the 1980s and 1990s, companies began to view logistics as more than simply a source of cost savings and recognize it as a source of enhancing product or service offerings as part of the broader supply chain process to create competitive advantage (Novack, Langley and Rinehart 1995; McDuffie, West, Welsh and Baker 2001). Consequently, SCM places a premium on the adoption of a crossfunctional, externally focused view of logistics (Manrodt, Holcomb and Thompson 1997).
Although anecdotal evidence has supported the conceptual linkage between logistics and improved firm performance (e.g., Bowersox 1978; Fuller, O'Connor and Rawlinson 1993), empirical research that establishes the link between strategic buyer-supplier relationships and logistics integration has been scarce. To help bridge this gap in literature, this study explores the connection between strategic buyer-supplier relationships and logistics integration, along with the subsequent impact on a firm's agility performance.
The notion of strategic buyer-supplier relationships has gained substantial momentum and supply chain partners work together to jointly plan and execute strategic initiatives aimed at achieving customer service improvements (Mohr and Spekman 1994). Accordingly, many firms are adopting a strategic supply management approach to guide their dyadic operations. Within this arrangement, the partners work together to leverage both their assets and capabilities toward better integration of the delivery activities to satisfy the ultimate needs of their customers. In this study, we first identify a parsimonious set of relational factors including (a) limited number of suppliers, (b) long-term relationship orientation, and (c) interfirm communication to form the domain of strategic buyer-supplier relationships so as to investigate its possible impact on logistics integration.
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The use of information technology as a means to enhance logistics integration has been touted in the literature. In particular, information technology has been promoted as an essential tool to ensure the logistics objective of providing timely service. Researchers have also examined the relationships between information technology, integration and performance (e.g., Daugherty, Sabath and Rogers 1992; Gustin, Stank and Daugherty 1994). Along similar lines, in this study, we specifically test the effect of information technology on external logistics integration. Another major contribution of this research is the further evaluation of information technology as a moderator of the relationship between strategic buyer-supplier relationships and logistics integration. In addition, we examine the impact of logistics integration on firms' agility performance because extant research, though anecdotal and disjointed, has stressed that agility is an essential ingredient of competitive advantage (Stalk and Hout 1990; Jayaram, Vickery and Droge 1999; Stank, Daugherty and Ellinger 1999).
The remainder of the paper is structured as follows. The next section develops a synthesis of the literature and the logic of the substantive relationships among the model's constructs and state formal hypotheses. The next section explains the research methodology, including data collection, instrument development and measures, hypothesis testing and results. The next section is about the discussion and implications of the study findings. The concluding section highlights some limitations of the study and offer suggestions for future research.
CONCEPTUAL FRAMEWORK AND HYPOTHESES
Drawing on the "relational view" of inter-organizational competitive advantage (Dyer and Singh 1998), the conceptual model linking strategic buyer-supplier relationships, information technology, external logistics integration and agility performance is depicted in Figure 1. This framework is also grounded in the paradigm of strategic management theory that emphasizes the development of collaborative advantage (e.g., Kanter 1994; Dyer 2000), as opposed to competitive advantage. Within the collaborative paradigm, the business world is composed of a network of interdependent relationships developed and fostered with the goal of deriving greater and mutual benefits (Chen and Paulraj 2004a). In the following subsections, the paper presents (1) a brief literature support for the theoretical constructs included in this study, and (2) the logic of the substantive relationships among the study variables and state hypotheses.
Strategic Buyer-Supplier Relationships and Logistics Integration
Strategic buyer-supplier relationships focus on initiatives that enhance superior relational characteristics between the supply chain members and create a win-win situation for both buyer and supplier firms (Paulraj and Chen 2005). As SCM is built on a foundation of trust and commitment (Kumar 1996), this study adopted three critical factors (limited number of suppliers, long-term relationship orientation and inter-firm communication) that have been documented to foster trust and commitment (Chen, Paulraj and Lado 2004) to underpin the domain of strategic buyer-supplier relationship. A brief literature and theoretical foundation for the three factors is presented below.
Increasingly, companies are emphasizing working closely and co-operatively with a limited number of suppliers that are trustworthy rather than using the traditional, arms-length, adversarial mode of conducting business with a large number of suppliers (Helper 1991; Guimaraes, Cook and Natarajan 2002; Ogden 2006). Researchers have documented that this relational contracting approach is a required element of strategic buyer-supplier relationships (Helper 1991; Dyer and Singh 1998; Ketchen and Giunipero 2004; Goffin, Lemke and Szwejczewski 2006). Apart from increasing trust and relational reliability, this approach provides benefits including (1) fewer suppliers to contact in case of orders given on short notice, (2) increased economies of scale based on order volume and the learning curve effect, (3) dedicated capacity and (4) better customer service and market penetration (De Toni and Nassimbeni 1999).
More and more supplier contracts are becoming long-term and many suppliers are providing information regarding their processes, quality performance and even cost structure to the buying firm (Helper 1991). Such close relationships mean that channel participants share risks and rewards and are oriented for long-term relationship (Kaufman, Wood and Theyel 2000; Kotabe, Martin and Domoto 2003). It is suggested that companies would gain benefits by placing a larger volume of business with fewer suppliers using long-term contracts (Hahn, Pinto and Brag 1983; Giunipero, Handfield and Eltantawy 2006). De Toni and Nassimbeni (1999) found that a long-term perspective between the buyer and supplier increases the intensity of buyer-supplier coordination, such as provision of technological and managerial assistance and exchange of information during product development and production stages. Moreover, through a long-term relationship, the supplier will become part of a well-managed chain and will have a lasting effect on the competitiveness of the entire supply chain (Choi and Hartley 1996; Chen et al. 2004).
Effective interfirm communication can be characterized as frequent, genuine, and involving personal interaction between buying and selling personnel (Carr and Pearson 1999; Krause 1999; Kocabasoglu and Suresh 2006). Numerous researchers have found that when buyers and suppliers communicate and share information relating to materials procurement and product design issues, they are more likely to (1) improve the quality of their products, (2) reduce customer response time, (3) reduce the costs of protecting against opportunistic behavior and (4) improve cost savings through greater product design and operational efficiencies (Carr and Pearson 1999; Kotabe et al. 2003; Prahinksi and Benton 2004; Giunipero et al. 2006).
Logistics provides industrial firms with time and space utilities (Caputo and Mininno 1998). Logistics integration can be internal and external (Stock, Greis and Kasarda 2000). Internal logistics integration refers to the logistics integration across functional boundaries within a firm. External logistics integration refers to the integration of logistics activities across firm boundaries. It reflects (1) a transformation of the manufacturing enterprise to encompass the entire supply chain, not just an individual company, as the competitive unit (Greis and Kasarda 1997), and (2) the extent to which the logistics activities of a firm are integrated with the logistics activities of its suppliers and customers. Higher levels of external logistics integration are characterized by increased logistics-related communication, greater coordination of the firm's logistics activities with those of its suppliers and customers, and more blurred organizational distinctions between the logistics activities of the firm and those of its suppliers and customers (Stock et al. 2000).
Instead of adversarial relationships, firms are increasingly placing their emphasis on the transformation of partnerships with their suppliers (Jones, Hines and Rich 1997). The underlying reason is that suppliers within a strategic relationship are more likely to be motivated to guarantee delivery, quality and even cost to the buyer firm, and are more willing to work closely to understand and incorporate the buyer firm's requirements into their own operations (Levy 1997). Strategic buyer-supplier relationships predominantly involve informal processes based on trust, mutual respect and information sharing, the joint ownership of decision and collective responsibility for outcomes (Griffin and Hauser 1996; Kahn 1996). Such collaboration between the buyer and supplier firms are essential to ensure delivery of high-quality services to customers, and facilitates the ability to seamlessly integrate logistics activities across organizational boundaries (Cavinato 2005). Researchers have empirically documented how relationship commitment and trust foster greater cooperation, reduce functional conflict and enhance integration as well as decision-making under conditions of uncertainty and ambiguity (Morgan and Hunt 1994). Moreover, as strategic buyer-supplier relationships, characterized by limited number of suppliers, long-term relationship orientation and interfirm communication, can facilitate complementary interactions among dyadic partners that can ultimately improve logistics coordination, we hypothesize that higher levels of strategic relationships can facilitate increased integration of logistics activities across the supply chain partners. Thus,
H1: Strategic buyer-supplier relationships will have a positive effect on external logistics integration.
Information Technology and Logistics Integration
More than ever before, information technology is permeating the supply chain at every point, transforming the way exchange-related activities are performed and the nature of the linkages between them (Palmer and Griffith 1998). Interorganizational systems are information and communication technology-based systems that transcend legal enterprise boundaries (Konsynski 1993). The goal of these systems is to replace inventory with higher-quality or near-perfect information. Research has shown information technology to be an effective means of promoting collaboration between collections of firms, such as groups of suppliers and customers organized into networks (Giunipero et al. 2006). The strength of interorganizational systems has been particularly important with respect to enabling the process transformation needed to create effective networks (Greis and Kasarda 1997; Christiaanse and Kumar 2000). These interorganizational information systems may include direct computer-to-computer links with suppliers or simple electronic data interchange (EDI) systems for exchanging data such as purchase orders, invoices and advice of delivery notices or may involve more complex transactions such as integrated cash management systems, shared technical databases, Internet, intranet and extranet (Min and Galle 1999).
Information technology is essential in supporting strategic as well as operational logistics decisions. Seamless material flows are achieved by replacing the notion of a sequential and linear chain of information exchange with a set of simultaneous information exchanges that span the members of the supply chain (Greis and Kasarda 1997; Monczka, Trent and Handfield 2004). Information technology enhances supply chain logistics efficiency by providing real-time information regarding product availability, inventory level, shipment status and production requirements (Radstaak and Ketelaar 1998). It helps in sharing information about markets, materials requirements forecasts, production and delivery schedules (Webster 1995). In particular, information technology (1) has vast potential to facilitate collaborative planning among supply chain partners by sharing information on demand forecasts and production schedules that dictate supply chain activities (Karoway 1997), (2) can effectively link customer demand information to upstream supply chain functions (e.g., supply management and manufacturing) and subsequently facilitate "pull" (demand driven) supply chain operations (Min and Galle 1999) and (3) help eliminate nonvalue adding activities by avoiding congestion in different supply chain partner firms (Lee 2004). Furthermore, as information technology may contribute to better integration by fostering communication-based competencies such as (1) dissemination and sharing of information, and (2) communication through proximity, frequent exchanges and collaborative interde-pendencies (Grover and Malhotra 1997; Carr and Smeltzer 2002; Vickery, Jayaram, Droge and Calantone 2003; Lee 2004; Sanders 2005), it is hypothesized that information technology can lead to better integration of the logistics activities and also moderate the relationship between strategic buyer-supplier relationships and logistics integration.
H2: Information technology has a positive effect on external logistics integration.
H3: Information technology moderates the relationship between strategic buyer-supplier relationships and external logistics integration.
Logistics Integration and Agility Performance
Agility, in this study, refers to supply chain partners' superior performance in flexibility, time, delivery and responsiveness, four critical facets that have been frequently discussed in logistics management literature. Flexibility, especially flexibility within logistics process, is one of the most important antecedents of supply chain agility (Swafford, Ghosh and Murthy 2006). Past researchers have also recognized the strategic importance of time-based performance (Droge, Jayaraman and Vickery 2004; Nahm, Vonderembse, Rao and Ragu-Nathan 2006). Subsequently, they have considered various aspects of time-based performance relative to different stages of the overall value delivery cycle and have proposed several measures to evaluate them (e.g., Jayaram et al. 1999; Droge et al. 2004). The frequent appearance of the measures including delivery speed (Vickery, Droge, Yeomans and Markland 1995) and delivery reliability/dependability (Handfield 1995) suggests the important effect of delivery performance on agility. In addition, the advent of time-based competition has elevated the strategic importance of customer responsiveness (Stalk and Hout 1990). Customer responsiveness describes a firm's ability to respond in a timely manner to customers' needs and wants. Thus, a firm's ability to respond promptly to customers' needs can be a source of enduring competitive advantage (Cusumano and Yoffie 1998). Among the benefits associated with superior customer responsiveness are (1) greater customer loyalty and likelihood of repeat purchase; (2) customers' increased willingness to pay premium prices for high-quality products and services; and (3) increased ability to continually improve the firm's product-delivery system and effectively adapt to strategic requirements (Stalk and Hout 1990). A recent study concludes that among the measures of time-based performance, customer responsiveness is rated as the highest in terms of strategic importance (Jayaram et al. 1999). As the logistics elements of flexibility, delivery speed, delivery reliability/dependability and customer responsiveness clearly represent key components of customer service for most companies (Fawcett, Stanley and Smith 1997; Swafford et al. 2006), these indicators are collectively included to measure agility performance.
An organization's performance is only as good as the weakest link in its supply chain. Accordingly, successful companies recognize that the creation of superior customer value is a function of the firm's logistics capability as well (Fawcett et al. 1997). Logistics, a pivotal coordination mechanism, help firms manage geographically dispersed global operations and facilitate agile just-in-time and other time-based competitive strategies (McGrath and Hoole 1992; Stock et al. 2000). This notion clearly reflects the importance of logistics as (1) a coordinating mechanism among multiple units of the enterprise, and (2) a source of customer value and competitive advantage (Vonderembse, Tracey, Tan and Bardi 1995; Stock et al. 2000). Therefore, in accordance with previous research that has shown a positive linkage between logistics integration and increased efficiency and productivity (Larson 1994; Gustin, Daugherty and Stank 1995; Frohlich and Westbrook 2001; Rosenzweig, Roth and Dean 2003; Sanders 2005), it is conjectured that logistics integration will have a significant impact on supplier and buyer agility.
H4: External logistics integration is positively related to agility of the supplier and buyer firms.
A cross-sectional mail survey in the United States was utilized for data collection. The target sample frame consisted of members of the Institute for Supply Management[TM] (ISM) drawn from firms covered under the two-digit SIC codes between 34 and 39 (34-Fabricated Metal Industries, 35-Industrial Machinery and Equipment, 36-Electronic and Other Electric Equipment, 37-Transportation Equipment, 38-Instruments and Related Products, 39-Miscellaneous Manufacturing Industries). These firms were selected as they are documented in the literature to be more advanced in the implementation of various supply chain initiatives.
The title of the specific respondent being sought was typically Vice President of Purchasing, Materials Management, and SCM or Director/Manager of Purchasing, Material Management. A seven-point Likert scale with end points of "strongly disagree" and "strongly agree" was used to measure the items. The performance indicators were measured using seven-point Likert scale with end points of "decreased significantly" and "increased significantly."
In an effort to increase the response rate, a modified version of Dillman's total design method was followed (Dillman 1978). All mailings, including a cover letter, the survey, and a postage-paid return envelope, were sent via first-class mail. Two weeks after the initial mailing, reminder postcards were sent to all potential respondents. To those who did not respond, a second mailing of surveys, cover letters, and postage-paid return envelopes were mailed approximately 28 days after the initial mailing. Of the 1,000 surveys mailed, 46 were returned due to address discrepancies. From the resulting sample size of 954, 232 responses were received, resulting in a response rate of 24.3 percent. A total of 11 were discarded due to incomplete information, resulting in an effective response rate of 23.2 percent (221/954). The final sample included 35 presidents/vice presidents (16 percent), 138 directors (62 percent), 33 supply managers (15 percent) and 15 others (7 percent). The respondents worked primarily for medium to large firms with nearly 36 percent working for firms employing more than 1,000 employees. Nearly 60 percent of the firms had a gross income of greater than $100 million. In general, with respect to the annual sales volume, the respondents were evenly distributed among the different groups. The respondents were also distributed evenly among the six SIC codes selected.
Nonresponse bias was tested in two stages. First, the sample and the population means of demographic variables, namely, number of employees and sales volume were compared to check for any significant difference. The t-tests performed yielded no statistically significant differences (at 99 percent confidence interval) between the sample and population. Additionally, the responses of early and late waves of returned surveys were compared to provide additional support of nonresponse bias (Armstrong and Overton 1977). Along with the 10 demographic variables, 30 randomly selected variables measuring various SCM constructs, including the ones contained in this study and others, were also included in this analysis. The final sample was spilt into two, based on the dates they were received. The early wave group consisted of 123 responses while the late wave group consisted of 98 responses. The t-tests performed on the responses of these two groups yielded no statistically significant differences (at 99 percent confidence interval). These results suggest that nonresponse may not be a problem.
As this study collected data from a single respondent in each responding firm, a test for potential common method bias was conducted. Methodologically, this potential problem can be tested by the Harman's single factor test (Harman 1967). According to this test, if common method bias exists, (1) a single factor will emerge from a factor analysis of all survey items (Podsakoff and Organ 1986), or (2) one general factor accounting for most of the common variance existing in the data will emerge (Doty and Glick 1998). An unrotated factor analysis using the eigenvalue-greater-than-one criterion revealed six distinct factors that accounted for 65 percent of the variance. The first factor captured only 26 percent of the variance in the data. As a single factor did not emerge and the first factor did not account for most of the variance, common method bias does not appear to be a problem (Frohlich and Westbrook 2001).
Measures and Instrument Development
The indicators used to measure the theoretical constructs are based on an extensive review of related literature. Items tapping the construct "Limited Number of Suppliers" measure the extent to which firms increasingly emphasize close, relational contracting with a smaller number of dedicated suppliers (Kekre, Murthi and Srinivasan 1995; Bozarth, Handfield and Das 1998; Shin, Collier and Wilson 2000). The construct "Long-Term Relationships Orientation" is operationalized by indicators reflecting the extent to which the buying firm (a) expects its relationships with key suppliers to last a long time, (b) works closely with key suppliers to improve product quality, and (c) views the suppliers as an extension of the company; in turn, (d) suppliers see their relationship with the buying firm as a long-term alliance (Krause and Ellram 1997; Shin et al. 2000). The construct "Inter-firm Communication" is operationalized to include the extent to which the firm and its key suppliers (a) share critical, sensitive information related to operational and strategic issues, (b) exchange such information frequently, informally and/or in a timely manner, (c) maintain frequent face-to-face meetings and (d) closely monitor and stay abreast of events or changes that may affect both parties (Krause and Ellram 1997; Carr and Pearson 1999; Carr and Smeltzer 1999).
The indicators of "Information Technology" are operationized to denote the presence of direct computer-to-computer links, electronic transactions and inter-organizational coordination achieved using electronic links, as well as the use of advanced information systems to track or expedite shipments (Radstaak and Ketelaar 1998; Carr and Pearson 1999). Finally, the construct of "Agility Performance" is measured by indicators tapping the firm's ability to respond in a timely manner to the needs and wants of its customers, through (a) flexibility, (b) delivery reliability, (c) prompt response, (d) rapid confirmation of orders and (e) rapid handling of customer complaints (Stalk and Hout 1990; Jayaram et al. 1999; Swafford et al. 2006).
Before data collection, the content validity of the instrument was established by grounding it in existing literature. Pretesting the measurement instrument before the collection of data further validated it. Researchers as well as supply management executives affiliated with ISM were involved in this process. These experts were asked to review the questionnaire for structure, readability, ambiguity and completeness (Dillman 1978). The final survey instrument incorporated minor changes to remove a few ambiguities that were discovered during this validation process. As indicated earlier, multi-item scales were developed to measure the theoretical constructs. The scales were tested for normality and outliers using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and the Bartlett test of sphericity. To assess the reliability of the study constructs, the average correlation among items in a scale is used (Cronbach 1951; Nunnally 1978). As can be seen in Appendices 1 and 2, except for limited number of suppliers, the Cronbach values ([alpha]) for all other constructs were above the cutoff value of 0.70 (Cronbach 1951; Nunnally 1978).
A second-order confirmatory factor analysis (CFA) was utilized to establish the internal consistency of the first-order factors measuring "strategic buyer-supplier relationships." The KMO score of 0.88 and the Bartlett test of sphericity of 1,259.76 (p<0.0001) suggest that the data exhibits normality. Both the principal component procedure from SPSS and the measurement models in LISREL were used. As anticipated, most of the indicators loaded onto their underlying first-order constructs during factor analysis using the principal components method. The eigen values for these factors were all above the 1.0 cutoff point, while the percentage of variation was around 70 percent. The factor loadings were also above the cutoff point of 0.30 (Hair, Anderson, Tatham and Black 1998). During second-order CFA, the items remaining from the principal component stage were used as indicators of the first-order factors, which in turn were used as indicators of the second-order construct "strategic buyer-supplier relationships." The values for the model fit indices including goodness of fit (0.94), adjusted goodness of fit (0.90), normed fit index (0.94), non-normed fit index (0.96), root mean square residual (0.07), root mean square error of approximation (0.04) and normed [chi square] (1.97) illustrate that the model fits the data well. The [R.sup.2] values for the indicators were above the cutoff value of 0.30 (Chen et al. 2004). The standardized coefficients and t-values for the individual paths (Appendix 1) further show that all the indicators are significantly related to their underlying theoretical constructs and thus the second-order representation of strategic buyer-supplier relationships is appropriate.
Construct validity and unidimensionality for the constructs of strategic buyer-supplier relationships, external logistics integration and information technology were established using a second measurement model. The first-order factors (limited number of suppliers, long-term relationship orientation and inter-firm communication) were used as indicators of the second-order construct strategic buyer-supplier relationships. The KMO score of 0.86 and the Bartlett test of sphericity of 1741.75 (p<0.0001) suggest that the data exhibits normality. Principal component procedure from SPSS and the measurement models in LISREL were used to test this model as well. The results of these analyses are provided in Appendix 2. As anticipated, most of the indicators loaded onto their underlying constructs during factor analysis using the principal components method. The eigen values for these factors were above the 1.0 cutoff point, while the percentage of variation was around 65 percent. The factor loadings were above the cutoff point of 0.30 (Hair et al. 1998). The values for the CFA model fit indices including goodness of fit (0.92), adjusted goodness of fit (0.90), normed fit index (0.92), non-normed fit index (0.96), root mean square residual (0.05), root mean square error of approximation (0.05) and normed [chi square] (1.62) illustrate that the model fits the data well and hence establish unidimensionality. The [R.sup.2] values for the indicators were above the cutoff value of 0.30 (Chen et al. 2004). The standardized coefficients and t-values for the individual paths as shown in Appendix 2 further suggest that all the indicators are significantly related to their underlying theoretical constructs and thus establish construct validity. During these analyses, indicators that did not have good psychometric properties were deleted from further consideration. These analyses of validity, reliability, and unidimensionality indicate that the theoretical definitions of strategic buyer-supplier relationships, information technology and external logistics integration all have good psychometric properties.
The hypothesized structural equation model (Figure 2), linking strategic buyer-supplier relationships, information technology, external logistics integration and agility performance measures was tested using LISREL with variance-covariance matrices for the latent variables and residuals used as input. The score for the latent variables was the summated average of the items within. These scores were used as single indicators for the corresponding latent variables. Various different structural equation modeling methodologies, based on the pioneering works of Kenny and Judd (1984), have been proposed to test the interaction (product-term) effects. This study adopts the methodology proposed by Jaccard and Wan (1996) to test the moderating effect of information technology. As this methodology involves an extremely complicated setup, a complete description of the procedure is omitted. Interested readers are referred to additional sources (e.g., Jaccard and Wan 1996; Schumacker and Marcoulides 1998) for technical information on the various approaches available to test interaction effects.
Appendix 3 presents the indicators and associated reliability values for performance constructs. The model parameters were estimated using the method of maximum likelihood (Joreskog and Sorbom 1999). Most of the model fit indices (given in Figure 3) satisfied the recommended cutoff values, illustrating that the model fits the data very well. The hypothesized relationships were tested using their associated t-statistics. T-values >1.65 or 1.98 or 2.576 were considered to be significant at the 0.10, 0.05, and 0.01 levels, respectively (Hair et al. 1998). All hypothesized relationships were found to be significant, of which three were significant at the 0.01 level and one was significant at the 0.10 level.
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Three of the hypotheses ([H.sub.1] ~ [H.sub.3]) linking strategic buyer-supplier relationships and information technology to external logistics integration were statistically significant and in the expected directions. Specifically, the paths linking (1) strategic buyer-supplier relationships and external logistics integration (b=0.39; p < 0.01), (2) information technology and external logistics integration (b=0.27; p < 0.01) and (2) the interaction (product) term and external logistics integration (b=0.10; p < 0.05) were statistically significant. The last hypothesis (H4) postulates a positive link between external logistics integration and agility performance. The parameter estimate for this path was also significant and in the expected direction (b=0.18; p < 0.01).
DISCUSSION AND IMPLICATIONS
This study contributes to the research stream on logistics integration by specifically investigating the relationships between strategic buyer-supplier relationships, information technology, external logistics integration and agility performance. In general, the results of this research provide empirical evidence that effective external logistics integration is engendered by strategic buyer-supplier relationships and information technology. This study further reveals that information technology can indeed moderate the positive link between strategic buyer-supplier relationships and external logistics integration. The findings of significant relationships between the antecedents, external logistics integration and agility performance, thus constitute a significant contribution to, and extension of, the literature in logistics management.
Organizational researchers have long believed that strategically managed supplier relationships help integrate information, material and personnel between the partner firms (e.g., Morgan and Hunt 1994; Chen and Paulraj 2004b). Drawing on this understanding, the support for hypothesis H1 linking strategic buyer-supplier relationships and logistics integration reflects that strategic relational partnerships could specifically lead to superior external logistics integration. The results also suggest that strategic buyer-supplier relationships help foster collaborative behavior that facilitate joint planning and processes beyond levels reached in less intensive trading relationships. When a smaller number of suppliers are used, the portion of demand shared with them is significantly higher, mandating a better integration of logistics activities. Moreover, as two firms endure the relationship over a longer period of time, they develop interaction routines and coordination mechanisms that help them disseminate and interpret information and better integrate their logistics activities. The results also support the contention that the exchange of information through interfirm communication is an essential condition for realizing the potential benefits of collaborative relationships. Apart from many economic benefits, interorganizational communication, by (1) reducing communication errors, (2) facilitating information knowledge sharing and (3) fostering learning as well as intuition, can ultimately increase integration between the supplier and buyer firms. Therefore, it can be concluded that, in contrast to adversarial relationships, strategic buyer-supplier relationships, characterized by limited number of suppliers, long-term relationships and interfirm communication, can engender higher levels of logistics integration between the supply chain partners.
Various researchers have suggested that information is a valuable logistics resource. In fact, information flow has rightly been recognized as equally important to materials flow in a value chain. A number of authors have suggested that by substituting information for physical inventories, the use of information technology can set the firm apart from its competitors. The significance of hypotheses H2 and H3 support this very notion. In addition, it further reveals that information technology not only has a direct impact on external logistics integration, but also moderates the relationship between strategic buyer-supplier relationships and external logistics integration. The significant direct relationship suggests that information technology can serve as a powerful mechanism in coordinating suppliers and their activities. More specifically, by providing real-time information regarding product availability, material requirements forecast, inventory level, shipment status, production requirements, production and delivery schedules, information technology can greatly enhance the ability to further narrow delivery windows or make adjustments to the existing schedules, thereby ultimately boosting the supply chain logistics efficiency. The significant indirect effect clearly suggests that information technology can facilitate the development and use of collaborative communication, thereby leading to superior logistics integration between supply chain partners. It also supports the theoretical perspective of resource complementarity, suggesting that information technology can contribute to integration through leveraging other human, social and relational competencies engendered through strategic buyer-supplier relationships.
Faced with an ever-increasing demand for better and faster service, firms are forced to explore all options for improving their ability to deliver service that immensely gratifies their customers. The empirical support for hypothesis H4 suggests that external logistics integration could be one of those strategic options that lead to higher percentage of agile and on-time delivery of products and service to customers. The result also shows that the seamless integration of the logistics activities such as distribution, transportation and/or warehousing facilities between supply chain partners is crucial for responsiveness, flexibility and dependability. The results further provide empirical evidence to the value-added potential of the logistics function, suggesting that external logistics should be managed as a vital strategic activity and that its coordination can ultimately generate a sustainable win-win strategic advantage through the improvement of agility performance of both the supplier and buyer firms.
Vertical disintegration, along with the globalization of markets, has led companies to recognize the value-added potential of the logistics function toward the achievement of sustainable competitive advantage. Research on the notion of external logistics integration has not been lacking. Although various factors that affect logistics integration have been discussed in the past, no study has attempted to systematically identify strategic buyer-supplier relationships as a potential driving force and empirically test its impact on external logistics integration. Realizing that strategic buyer-supplier relationships is a profoundly complex concept, this study identifies the factors of limited number of suppliers, long-term relationship orientation, and interfirm communication to form its domain, and further test its subsequent effect on logistics integration. Moreover, the moderating effect of information technology on the relationship between buyer-supplier relationship and logistics integration is another premiere contribution of this study. The results of the structural equation model were all found to be significant and support the notion that external logistics integration is of strategic importance as it can have a significant influence on the agility performance of both supplier and buyer firms. The impact of strategic buyer-supplier relationships and information technology on external logistics integration provide empirical support to the notion that these constructs can help firms improve the integration of their logistics activities through superior relational and technological initiatives.
At this point, the authors acknowledge some limitations of this study that might provide opportunities for future research. In this study, three factors have been identified to represent the concept of strategic buyer-supplier relationships. This concept, however, is a profoundly complex construct and thus future research may need to include other factors such as trust and commitment, supplier selection, supplier integration and supplier certification. Another limitation of this research concerns the sample population. Having drawn from a list of ISM members, we can only claim that the results of this research are generalizable to firms in that population. Although this study sample covered a wide range of firms in the ISM database in terms of industry membership and demographic variables, future research may include a broader population of firms, including those in service industries in order to expand the scope of generalizability of the results. Furthermore, while firms within SIC codes between 34 and 39 are generally more advanced in the implementation of various supply chain initiatives, compared with those in other industries, the results of this study may not be generalized to all industries. Finally, this study focused on the buyer-supplier dyad as the unit of analysis, and assumed the buying firm's perspective. Thus, there is a need to more fully examine the nature of the exchange relationship from the supplier's perspective so as to establish whether or not the relationship is reciprocal and mutually beneficial. Despite these limitations, this study paves the way for researchers and managers to more fully capitalize on the potential of external logistics integration in creating collaborative advantages for both buyer and supplier firms.
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Appendix 1 Strategic Buyer-Supplier Relationships: Second-Order Factor Analysis Results Principal Indicator Component Measurement Model (Cronbach's Alpha, Eigen Factor Std. value) Loading Coefficient [R.sup.2] t-Value First-order factors Limited number of suppliers ([alpha]= 0.65; Eigen value= 1.60) We rely on a small 0.83 0.69 0.46 - number of high-quality suppliers We maintain close 0.82 0.72 0.54 6.25 relationship with a limited pool of suppliers We get multiple price quotes from suppliers before ordering* We drop suppliers for price reasons* We use hedging contracts in selecting our suppliers* Long-term relationship orientation ([alpha]=0.85; Eigen value=2.83) We expect our 0.85 0.67 0.63 - relationship with key suppliers to last a long time We work with key 0.67 0.74 0.72 9.24 suppliers to improve their quality in the long run The suppliers see 0.87 0.79 0.48 11.90 our relationship as a long-term alliance We view our 0.74 0.85 0.51 10.07 suppliers as an extension of our company We give a fair profit share to key suppliers* The relationship we have with key suppliers is essentially evergreen** Inter-firm communication ([alpha]=0.86; Eigen value=3.28) We share sensitive 0.69 0.59 0.35 - information (financial, production, design, research, and/or competition) Suppliers are 0.72 0.68 0.46 8.91 provided with any information that might help them Exchange of 0.83 0.87 0.75 9.25 information takes place frequently, informally and/or in a timely manner We keep each other 0.78 0.86 0.74 9.15 informed about events or changes that may affect the other party We have frequent 0.76 0.71 0.50 8.03 face-to-face planning/ communication We exchange performance feedback* Second-order factor-SBSR ([alpha]=0.74) Limited number of 0.68 0.47 6.52 suppliers Long-term 0.78 0.61 7.83 relationship orientation Inter-firm 0.89 0.80 7.67 communication *Items dropped after exploratory factor analysis. **Items dropped after confirmatory factor analysis. Model fit indices: Normed [chi square]=1.97 ([less than or equal to]5.0); Goodness of fit index=0.94 ([greater than or equal to]0.90); Adjusted Goodness of fit index=0.90 ([greater than or equal to]0.80); Normed fit index=0.94 ([greater than or equal to]0.90); Non-normed fit index=0.96 ([greater than or equal to]0.90); Comparative fit index=0.97 ([greater than or equal to]0.90); Root mean square residual=0.07 ([less than or equal to]0.10); Root mean square error of approximation=0.04 ([less than or equal to]0.10) Appendix 2 Factor Analysis Result for Constructs included in the Proposed Model Principal Indicator Component Measurement Model (Cronbach's Alpha, Eigen Factor Std. value) Loading Coefficient [R.sup.2] t-Value Strategic buyer-supplier relationships ([alpha]=0.74; Eigen value=2.10) Limited number of 0.81 0.54 0.30 - suppliers Long-term relationship 0.74 0.67 0.45 6.98 orientation Inter-firm 0.78 0.88 0.78 6.93 Communication Information technology ([alpha]=0.84; Eigen value=3.43) There are direct 0.77 0.69 0.47 - computer-to-computer links with key suppliers Inter-organizational 0.72 0.73 0.54 9.36 coordination is achieved using electronic links We use information 0.77 0.80 0.64 9.97 technology-enabled transaction processing We have electronic 0.64 0.56 0.31 7.39 mailing capabilities with our key suppliers We use electronic 0.68 0.55 0.30 8.16 transfer of purchase orders, invoices and/or funds We use advanced 0.74 0.73 0.53 9.30 information systems to track and/or expedite shipments External logistics integration ([alpha]=0.92; Eigen value=4.16) Inter-organizational 0.78 0.74 0.55 - logistic activities are closely coordinated Our logistics 0.84 0.79 0.62 14.43 activities are well integrated with the logistics activities of our suppliers We have a seamless 0.86 0.85 0.72 12.46 integration of logistics activities with our key suppliers Our logistics 0.84 0.84 0.71 12.38 integration is characterized by excellent distribution, transportation and/or warehousing facilities The inbound and 0.87 0.86 0.74 12.59 outbound distribution of goods with our suppliers is well integrated Information and 0.65 0.65 0.42 9.27 materials flow smoothly between our supplier firms and us Model fit indices: normed [chi square]=1.62 ([less than or equal to]5.0); goodness of fit index=0.92 ([greater than or equal to]0.90); adjusted goodness of fit index=0.90 ([greater than or equal to]0.80); normed fit index=0.92 ([greater than or equal to]0.90); non-normed fit index=0.96 ([greater than or equal to]0.90); comparative fit index=0.97 ([greater than or equal to]0.90); root mean square residual=0.05 ([less than or equal to]0.10); root mean square error of approximation=0.05 ([less than or equal to] 0.10) Appendix 3 Indicators measuring Agility Performance Agility performance ([alpha]=0.86) Supplier performance Volume flexibility Scheduling flexibility On-time delivery Delivery reliability/consistency Prompt response Buyer performance Volume flexibility Delivery speed Delivery reliability/dependability Rapid confirmation of customer orders Rapid handling of customer complaints
Antony Paulraj is an assistant professor of operations management in the Coggin College of Business at the University of North Florida in Jacksonville, Florida.
Injazz J. Chen is a professor of operations management in the Nance College of Business Administration at Cleveland State University in Cleveland, Ohio.
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|Author:||Paulraj, Antony; Chen, Injazz J.|
|Publication:||Journal of Supply Chain Management|
|Date:||Mar 22, 2007|
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