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Journal of Operations Management 22 (2004) 39­62

Supplier evaluations: communication strategies to improve supplier performance

Carol Prahinski a, , W.C. Benton b,1

b

Richard Ivey School of Business, Operations Management Area Group, University of Western Ontario, London, Ontario, N6A 3K7 Canada Fisher College of Business, Department of Management Sciences, The Ohio State University, Columbus, OH 43210, USA Received 1 July 2002; received in revised form 7 October 2003; accepted 1 December 2003

a

Abstract As firms increasingly emphasize cooperative relationships with critical suppliers, executives of buyer firms are using supplier evaluations to ensure that their performance objectives are met. Supplier evaluations, one type of supplier development program (SDP), are an attempt to meet current and future business needs by improving supplier performance and capabilities. The purpose of this study was to determine how suppliers perceive the buying firm's supplier evaluation communication process and its impact on suppliers' performance. Three communication strategies (indirect influence strategy, formality and feedback) were tested separately and one in unison (collaborative). Using structural equation modeling (SEM) and data collected from 139 first-tier North American automotive suppliers, the results of this research have shown that, contrary to the SDP literature from the buying firm's perspective, the supplier's perceptions of the buying firm's communication does not directly influence suppliers' performance. Specifically, the supplier evaluation communication process does not ensure improved supplier performance unless the supplier is committed to the buying firm. Buying firms can influence the supplier's commitment through increased efforts of cooperation and commitment. The results also indicate that when a buying firm utilizes collaborative communication, the supplier perceives a positive influence on the buyer­supplier relationship. © 2004 Elsevier B.V. All rights reserved.

Keywords: Supply chain management; Supplier evaluations; Supplier development; Supply chain communication strategies

1. Introduction In today's business environment, there is an emphasis on developing long-term cooperative relationships with critical suppliers. Business managers are reducing their supply base and thereby increasing the buying volume with the remaining suppliers.

Corresponding author. Tel.: +1-519-661-3305. E-mail addresses: [email protected] (C. Prahinski), [email protected] (W.C. Benton). 1 Tel.: +1-614-292-8868.

Many executives are hesitant to rely on an untested supplier without first taking the time to build an effective relationship to ensure specific performance objectives. When a supplier is unable to conform to the buying firm's expectations, the buying firm manager must determine the most appropriate action to resolve the issue. To maintain the working relationship, the manager must find a way to communicate the problem and motivate the supplier to change its results. The research framework herein will focus on the suppliers' perceptions of a buying firm's attempts to motivate

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suppliers through supplier development programs, and in particular, supplier evaluations. The buying firm develops the supplier evaluation, or report card, and communicates the results to its suppliers with the hope and expectation that the supplier will address noted shortcomings (Morgan, 2001; Purdy et al., 1994). Many supplier development programs (SDPs), however, are not successful (Krause et al., 2000; Monczka et al., 1993; Porter, 1991; Purdy et al., 1994; Watts and Hahn, 1993). A number of studies have emphasized the need to determine the contributing factors of SDP success or failure (Krause et al., 2000; Purdy et al., 1994). To our knowledge, a study by Krause et al. (2000) was the only study that considered the importance of SDPs on supply chain performance. Two studies have addressed the buying firm's perspective of the impact of supplier evaluations on the buyer­supplier relationship (Krause, 1999; Carr and Pearson, 1999). To date, there has been little investigation of the suppliers' reactions to SDPs and the impact of supplier evaluation communication on the suppliers' performance. It is not known whether SDPs are effective in improving the supplier's performance. The purpose of this research is to assess the supplier's perceptions of four buying firm's supplier evaluation communication strategies (indirect influence strategy, formality, feedback and collaborative communication) and determine how specific communication strategies influence suppliers' performance. The supplier's perceptions of the buyer­supplier relationship and the supplier's commitment to the buying firm are tested as possible mediators. This study is important because a buying firm's performance increasingly hinges on the capabilities of its supply base. The following research questions are investigated from the supplying firm perspective: (1) is the impact of the buying firm's strategy for communicating supplier performance evaluations mediated by the buyer­supplier relationship and supplier's commitment? (2) Do suppliers perceive that the buying firm's communication of the evaluation affects their performance? In the following section, the relevant literature is reviewed. The conceptual model and research hypotheses are then developed. Subsequently, the research methodology is described. The analysis and results are

presented in section five. The paper concludes with a comprehensive discussion and conclusions. 2. Literature review The literature review is organized into five sections: supplier development programs with an emphasis on supplier evaluations, inter-organizational communication strategies, buyer­supplier relationships, supplier's commitment, and supplier's performance. 2.1. Supplier development programs--supplier evaluations SDPs are defined as activities undertaken by the buying firms in their efforts to measure and improve the products or services they receive from their suppliers. From the buyer's perspective, SDPs are warranted when the buying firm perceives that the current supplier base is unable to meet short and long-term business objectives (Handfield et al., 2000). The buying firms' typically selects a small number of critical suppliers to focus their improvement effort (Watts and Hahn, 1993). Although there are several different types of SDPs (Krause, 1997), the supplier evaluation process was selected as the main focus of this research because the buying firm's assessment of the supplier's performance was considered a catalyst for all SDP's. Based on the evaluation process, the buying firm can determine if the supply base is capable of meeting current and future business needs. The buying firm needs to quantify and communicate the measurements and targets to the supplier so that the supplier is made aware of the discrepancy between its current performance and the buying firm's expectations. Without an effective measurement and communication system, the inter-organizational coordination and improvement initiatives would be ineffective. To our knowledge, no research has directly addressed different supplier evaluation communication strategies. Supplier evaluations could include both process and content (Hartley and Choi, 1996; Porter, 1991; Purdy et al., 1994), however, many recent studies emphasize only the quality performance aspect (e.g., Forker et al., 1999; Park et al., 2001). There has been no comprehensive survey research

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on supplier evaluation and SDP from the supplier's perspective. 2.2. Inter-organizational communication Communication has been described as "the glue that holds together a channel of distribution" (Mohr and Nevin, 1990). According to Mohr and Nevin (1990), there are four categories of communication: content, medium, feedback and frequency. In order to adequately assess communication, three of the four components were included in this study and are further described below. Communication frequency was excluded since it is often measured with other communication constructs, predominantly content or medium (e.g., Boyle and Alwitt, 1999; Hartley et al., 1997; Mohr and Sohi, 1995). Content refers to the message that is transmitted. Two predominant subcategories are: the type of information exchanged and the type of influence strategy embedded in the exchange, such as direct or indirect influence. Indirect influence strategy is designed to change the recipient's beliefs and attitudes, such as through education and communication of the evaluation, so that the recipients have more complete knowledge for decision-making (Boyle and Dwyer, 1995; Frazier and Sheth, 1985). Since direct influence strategy, such as power influence, was studied from the supplier's perspective in Maloni and Benton (2000), this research will examine indirect influence strategy with supplier evaluations as the type of information exchanged.2 Communication medium refers to the method used to transmit information. Two predominant classification schemes include: medium richness and formality. Medium richness is defined as the number of cues that can be used by the receiver to interpret the message and ranges from face-to-face, which is considered the richest medium, to electronic data transfer which is considered the least rich medium (Daft and Lengel, 1986). Formality assesses the structure and routine of

2 In Krause et al. (2000), the term for the buying firms' perceptions of indirect influence strategy was "direct involvement," where the buying firm directly involves itself in the supplier development effort. Since the focus of our research is on the suppliers' perceptions of the embedded communication strategy rather than the development effort expended by the buying firm, we will refer to the construct as "indirect influence strategy."

the communication (Carr and Pearson, 1999; Mohr and Sohi, 1995). Because of the categorical nature of medium richness, communication formality will be studied in this research. Formality is defined as the degree to which the inter-organizational communication of the supplier evaluation is established through structured rules and fixed procedures. Communication feedback, also called bi-directionality, refers to two-way communication between two firms (Mohr and Sohi, 1995; Purdy et al., 1994). This research will assess feedback as the supplier attempts to discuss the buying firm's evaluation of the supplier's performance. The focus is on clarifying the expectations and the evaluation process. Mohr and Nevin (1990) proposed that the dimensions of communication would function together in a specific combination based on channel conditions. They coined the phrase "collaborative communication strategy," which was more likely to occur in relational structures, supportive climates and symmetrical power. As in Mohr et al. (1996), collaborative communication is defined in this research as a communication effort that emphasizes indirect influence strategy, formality and feedback in unison. There are several gaps in the communication literature. First, the influence of the various types of communication strategies on the supplier's performance is unknown. Several studies from the buying firm's perspective assessed the indirect influence strategy or formality on the buying firm's performance (D'Amours et al, 1999; Krause et al., 2000; Walton and Marucheck, 1997). From the supplier's perspective, to our knowledge only one study has focused on the impact of indirect influence strategy on one performance measure, JIT shipment performance (Srinivasan et al., 1994). Moreover, there are no studies that have investigated the supplier's perspective of the buying firm's communication on supplier's performance. To date, there have not been any studies to determine whether the supplier evaluation communication strategy has an effect, either directly or indirectly, on the supplier's performance. Although several studies have stated that they expect a direct effect (e.g., Krause et al., 2000), the relationship has not been empirically tested. Third, if there is an indirect effect, the literature suggests that the buyer­supplier relationship (BSR) may be a mediator (Johnston and Lewin, 1996).

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2.3. Buyer­supplier relationships In the BSR literature, two predominant classifications exist. The first is based on the relationship as a transformation process, e.g., from awareness, exploration, expansion, and commitment to dissolution (Dwyer et al., 1987). The second classification is based on the components of the BSR at one point in time, such as structural governance ranging from a transactional-based relationship to a strategic alliance or vertical integration (Cooper and Gardner, 1993; Webster, 1992), or the continuum between competitive and cooperative orientation (Ellram and Hendrick, 1995). Researchers are increasingly assessing multiple dimensions of the characteristics that sustain the relationship or partnership (Boyle et al., 1992; Cannon and Perreault, 1999; Heide and John, 1990; Lambert et al., 1996; Maloni and Benton, 2000). For this research, BSR is defined as the supplier's perception of the buying firm's behavioral and operational relationship attributes: buying firm's commitment, cooperation and operational linkages. Although many possible dimensions could be included in the study (e.g., Johnston and Lewin, 1996; Lambert et al., 1996; Wilson, 1995), these three dimensions provided a representative sample of several important relationship characteristics, both behavioral and operational. Two theories were relied upon in the development of BSR: transactional cost analysis and social exchange theory (e.g., Blau, 1964; Ring and Van de Ven, 1994). Based on prior empirical research, commitment was shown to contain three components: investment in the trading partner, affective commitment and the expectation of the relationship extending into the future (Kumar et al., 1995). For this research, the buying firm's commitment was defined as the suppliers' perception of the degree to which the buying firm feels pledged or obligated to continue business with a specific supplier. This commitment can be reflected by loyalty, willingness to make investments in the supplier's business, and confidence in the stability of a long-term relationship (Anderson and Weitz, 1992). Based on prior empirical research, cooperation was shown to contain market flexibility (Boyle et al., 1992; Heide and Miner, 1992) and problem solving (Cannon and Perreault, 1999; Heide and Miner, 1992). For the current study, cooperation is defined as the supplier's perceptions of the degree to which the two trading part-

ners work together to solve problems, establish strategic directions and achieve their mutual goals (Cannon and Perreault, 1999; Maloni and Benton, 2000). Operational linkages were considered important since it would permit information exchange to be measured on a tactical level. The operational linkages construct is defined as the supplier's perceptions of the degree to which the buying and selling firms coordinate their systems, procedures and routines to facilitate operations (Cannon and Perreault, 1999). 2.4. Supplier's commitment Anderson and Weitz (1992) found that each channel member's commitment to the relationship was based on its perceptions of the other party's commitment. They found that the buying firm's commitment positively influences the supplier's commitment. Later, Krause (1999) found that the buying firm's perceptions of the supplier's commitment positively influenced the buying firm's commitment to the supplier. In the current study, supplier's commitment is defined as the degree to which the supplier feels obligated to continue business with the particular buying firm. In several studies, commitment was based on investments, as developed in the transactional cost analysis literature (e.g., Cannon and Perreault, 1999; Heide and John, 1990). For this study, we chose to expand our definition to include loyalty and longevity, as developed in the social exchange theory (Blau, 1964; Ring and Van de Ven, 1994). 2.5. Supplier's performance Business performance improvement is at the heart of supplier development programs. Mentzer and Konrad (1991) stated that performance measurement is the evaluation of effectiveness and efficiency of completing a given task. Effectiveness is the extent to which goals are accomplished. Efficiency is a measure of how well resources are utilized. Venkatraman and Ramanujam (1986) focused on organizational effectiveness, and classified business performance measures as either financial or operational (non-financial). Operational measures of performance can be classified in two streams: key competitive success factors (e.g., quality, delivery, price, service, and flexibility)

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and internal indicators, such as defects, schedule realization and cost. In the current study, the supplier's performance is an operational measure of key competitive success factors, namely product quality, delivery performance, price, responsiveness to change requests, service support, and overall performance. The supplier's performance directly influences the buying firm and is, therefore, a critical criterion for the buying firm. 3. Conceptual model and hypotheses The research herein will test the linkages among several constructs: supplier evaluation communication strategy, buyer­supplier relationship, supplier commitment and supplier performance. The relationships among several constructs are hypothesized and the general hypothesized model is shown in Fig. 1. Four models are developed: three communication strategies (indirect influence strategy, formality and feedback) are each tested in separate models and in the fourth model, the three strategies are tested in unison (called collaborative communication). 3.1. Dimensions of communication and their influence on the buyer­supplier relationship 3.1.1. Indirect influence strategy Indirect influence strategy, such as with education, training and site visits between two trading part-

ners, would integrate the businesses together with a common language and shared objectives. Boyle and Alwitt (1999) found that as information sharing and overall frequency of contact increased, BSR was enhanced. Sibley and Michie (1982) found that indirect influence strategy positively influenced the BSR cooperation dimension. To date, the impact of indirect influence strategy on BSR has not been investigated. The formal hypothesis is given below: H1a . Indirect influence strategy expressed by the buying firm to the supplier positively influences the buyer­supplier relationship. 3.1.2. Formality Carr and Pearson (1999) found that formal communication of supplier evaluations positively influenced BSR. Similarly, Vijayasarathy and Robey (1997) found that communication formality had a positive influence on cooperation, one dimension of BSR. Mohr and Sohi (1995) found that formality negatively influenced distortion and withholding of information. All three studies addressed formality and aspects of the relationship from the buying firm's perspective. It is anticipated that formality will positively influence the buyer­supplier relationship from the supplier's perspective. The formal hypothesis is given below: H1b . Communication formality established between the buying firm and the supplier positively influences the buyer­supplier relationship.

Cooperation Buying Firm's Commitment Operational Linkages

H3 Buyer-Supplier Relationship

Supplier's Commitment

H1a, 1b, 1c, 1d Supplier Evaluation Communication Strategy

H4

H5 Supplier's Performance

H2a, 2b, 2c, 2d

Fig. 1. Hypothesized Model.

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3.1.3. Feedback Anderson et al. (1987) found that channel members allocated more time to suppliers with whom they have good communication, participation and feedback. Later, Anderson and Weitz (1989) found mixed results regarding the impact of communication feedback on BSR. As such, these studies did not clearly establish the influence of feedback on BSR. It is anticipated that supplier evaluation feedback opportunities will eliminate ambiguity, leading to a closer perceived relationship, enhanced commitment and cooperation between the supplier and buying firm. The specific hypothesis is given below: H1c . Communication feedback between the buying firm and the supplier positively influences the buyer­supplier relationship. 3.1.4. Collaborative communication Collaborative communication coupled with the supplier development program would more fully integrate the buying firm and supplier. For the buying firm, the focus of the SDP is on meeting its current and future needs through a reduced number of suppliers. The development of BSR is integral to the SDP communication effort. As a recipient of the SDP communication effort, suppliers can achieve their business objectives of cost minimization, market growth and future sales with an improvement of BSR. From the buying firm's perspective, Mohr et al. (1996) found that collaborative communication was significantly related to commitment, coordination and satisfaction. They measured collaborative communication as more frequent medium richness, feedback, formality and indirect influence. By using collaborative communication, it is hypothesized that the supplier's perceptions of BSR will be positively influenced. The SDP communication effort could be interpreted as an example of the buying firm's commitment and cooperation attempts. The specific hypothesis is given below: H1d . Collaborative communication between the buying firm and the supplier positively influences the buyer­supplier relationship.

3.2. The influence of communication strategy on performance 3.2.1. Indirect influence strategy In previous studies, researchers found that an increase in the indirect influence strategy, such as with education programs, EDI communication and information sharing, improves the buying firm's performance as measured by cost (D'Amours et al., 1999), delivery performance (Walton and Marucheck, 1997), sales, time, product design, and quality (Krause et al., 2000). Each of these studies addressed the indirect influence strategy from the buying firm's perspective. Srinivasan et al. (1994) considered the impact of one specific type of indirect influence strategy, EDI technology, on JIT shipment performance from the automotive supplier's perspective. They found that when the buying firm shared their JIT schedule, delivery performance improved and shipment discrepancies decreased. The hypothesis is given as: H2a . Indirect influence strategy expressed by the buying firm (source) to the supplier (target) positively influences the supplier's performance. 3.2.2. Formality Formality of the evaluation process in relation to supplier's performance has been assessed in the SDP literature. Krause et al. (2000) found that formality of supplier evaluations did not have a direct impact on the buying firm's performance, but rather, it was mediated by the buying firm's direct involvement through site visits, training and education programs. [As noted earlier, we use the term indirect influence strategy rather than direct involvement to represent the suppliers' perspective.] It is hypothesized in the current study that with routine and formal communication, the supplier evaluation process will create a more effective conduit in communicating the buying firm's operational targets and expectations. When the supplier understands the buying firm's expectations, they can effectively manage their business operations to meet the needs and specifications of the buying firm. It is hypothesized that the formality of the supplier evaluation process positively influences supplier's performance.

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H2b . Communication formality established between the buying firm and the supplier positively influences the supplier's performance. 3.2.3. Feedback When managers in firms conduct dialogue, feedback and clarification, they attempt to reduce the equivocality and uncertainty in the relationship (Daft and Lengel, 1986). In turn, organizational performance would be improved since the participants will have a clearer understanding of the requirements. To our knowledge, the influence of communication feedback between two firms on organizational performance has not been empirically tested. In the job characteristics model in the organizational behavior literature, job performance feedback impacts personal and work-related outcomes, such as work performance quality, work satisfaction, absenteeism and turnover (Robbins, 2001). Several studies found support that high performance work practices, of which feedback is a component, impact organizational financial performance (Huselid, 1995; Huselid et al., 1997). The organizational behavior literature provides some support for the impact of individual communication feedback on organizational performance; however, inter-organizational feedback between buying and selling firms has not been tested. The following hypothesis is: H2c . Communication feedback between the buying firm and the supplier positively influences the supplier's performance. 3.2.4. Collaborative communication Collaborative communication measures three communication strategies in unison, indirect influence strategy, formality and feedback. From our review of the literature, Mohr et al. (1996) found that collaborative communication was significantly related to all three of their outcome measures: commitment, satisfaction and coordination. We were unable to find prior empirical research testing the relationship between collaborative communication and business performance. This research will test if collaborative communication influences supplier's performance. The hypothesis is given below.

H2d . Collaborative communication between the buying firm and the supplier positively influences the supplier's performance. 3.3. Buyer­supplier relationship and supplier's commitment The marketing literature contains some evidence that the buyer­supplier relationship has some influence on the supplier's commitment to the buying firm. From the dyadic perspective, Anderson and Weitz (1992) found that supplier's commitment was a function of three components of the buyer­supplier relationship: the buying firm's commitment as perceived by the supplier, the supplier's investment in the relationship, and communication feedback. It is anticipated that the supplier's commitment is a function of BSR. Therefore, the following hypothesis is: H3 . The buyer­supplier relationship positively influences the supplier's commitment to the buying firm. 3.4. Buyer­supplier relationship and supplier's performance One key to improving suppliers' short-term productivity benefits and long-term strategic advantages is to effectively manage the partnering relationship (Stuart, 1993). Many researchers have found that closer unidimensional buyer­supplier relationships positively influence performance measures, such as ROI (Carr and Pearson, 1999); order lead-time, service levels, out-of-stock situations, inventory levels (Vijayasarathy and Robey, 1997); quality, delivery reliability, lead-time and on-time delivery (Shin et al., 2000). Maloni and Benton (2000) found that a strong buyer­supplier relationship was perceived to have a beneficial influence on supply chain performance. However, several researchers concluded that a close relationship is not universally desirable (e.g., Cannon and Perreault, 1999; Heide and John, 1990; Noordewier et al., 1990). These researchers found that certain dimensions of the relationship construct can influence either positively or negatively the performance outcome. Heide and John (1990) found that close relationships are only useful when specific assets and uncertainty evoke a need to protect and to adapt. Cannon and Perreault (1999) found

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that procurement obstacles and importance of supply discriminate among the different types of relationships. Each of these studies relied on transaction­cost theory. As described in Section 2.3, for this research, BSR is based on both transaction­cost theory and social exchange theory. From the supplier's perspective, it is anticipated that BSR will have a positive impact on performance, as delineated in the following hypothesis: H4 . The buyer­supplier relationship positively influences the supplier's performance. 3.5. Supplier's commitment and supplier's performance When the supplier is committed to a buying firm, the supplier will want to ensure the continued success of the business relationship and therefore, meet and/or exceed the needs of the buying firm. Consequently, the supplier's commitment should influence the supplier's performance. Empirical research that directly measures the impact of supplier's commitment on performance was not found. In the organizational behavior literature, employee commitment to their firm was found to influence job performance (e.g., Riketta, 2002). Arthur (1994) found that organizations that use systems to develop higher employee commitment had higher productivity, lower scrap rates and lower employee turnover. However, individual-based and intra-organizational measures are significantly different from organization-based and inter-organizational measures. Therefore, the hypothesis is given below: H5 . The supplier's commitment to the buying firm positively influences the supplier's performance.

implied causal relationships among the research variables, is shown in Fig. 1. Given the many linked, causal relationships in the model, structural equation modeling (SEM) was selected as the most appropriate research methodology. SEM enables us to concurrently test the hypothesized relationships for each model. 4.1. Second-order factors In the four structural equation models, there are two second-order factors, collaborative communication and BSR, each of which is modeled to influence three first-order factors. Second-order factors are completely latent and unobservable since the covariation among the first-order factors is explained by the second-order factor (Byrne, 1995). The first-order factors are considered consequences (endogenous variables) of the second order factor. (See Byrne, 1995; Gorsuch, 1983; Hair et al., 1998 for more information). There are several benefits of using a second-order factor. First, each of the first-order factors is significantly correlated and thereby, the second-order factor increases the breadth of generalizability (Gorsuch, 1983). Second, when compared to aggregation, second-order factors retain the number of parameters in the model and thereby maximize the degrees of freedom for estimating the path coefficients. Third, due to the higher level of degrees of freedom, statistical power is higher. Fourth, measurement error is captured within the model. Finally, the outside influences on the first-order factors are captured in the model (Bollen, 1989). One concern of the second-order factor analysis is that when there are a high number of manifest variables per factor, the model has a higher level of measurement error, which negatively influences model fit (Bagozzi and Heatherton, 1994). In studies that utilize a high number of manifest variables per factor, the cutoff criteria for fit indices in second-order factor models may need to be relaxed. For this study, our objective was to enhance the generalizability of collaborative communication and buyer­supplier relationship based on empirical methods that supported a high-powered model. The results of a second-order factor model, such as the measurement error and outside influences, are richer as compared to the aggregated model.

4. Methodology Four models were developed to assess the influence of each supplier evaluation communication strategy (indirect influence, formality, feedback and collaborative communication) on BSR and supplier's performance. The hypothesized model, which depicts the

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4.2. The survey In the development of the survey instrument, previously tested and validated instruments were relied on wherever possible (such as Anderson and Weitz, 1992; Cannon and Perreault, 1999; Carr and Pearson, 1999; Krause, 1995; Maloni and Benton, 2000; Mohr and Sohi, 1995; Mohr and Spekman, 1994). Where applicable, minor wording changes were made to reflect the supplier's perspective rather than the buying firm's perspective. New items for feedback, formality, cooperation, commitment and operational linkages constructs were developed. Extensive validation steps were implemented for the new constructs. Content validity was supported by (1) an extensive literature review, (2) in-depth interviews with two automotive manufacturing executives, which provided an understanding of BSR, the supplier evaluation communication process and business performance, and (3) a pre-test of the survey by three first-tier automotive supplier executives and five experienced researchers, providing suggestions on wording and format modifications. There were four versions of the survey instrument, each of which asked the supplier to characterize a specific automobile manufacturer: Daimler-Chrysler, Ford, General Motors or Honda. 4.3. The sample The decision to study the communication between first-tier automotive suppliers and automobile manufacturer was based on the following: (1) the relationships between suppliers and the automobile manufacturers are well developed and fairly stable; many of the buyer­seller relationships have developed over decades. (2) Suppliers within the automobile industry represent a wide diversity of industries. Thus, conclusions drawn from the study may be generalized across a variety of businesses. (3) The automobile industry has been well studied, so results drawn from this study would fit within our knowledge base of the industry. Only critical suppliers, as defined by the buying firms, were selected for this study. The four largest North American automotive manufacturers were selected. Honda and DaimlerChrysler provided contact information for their most critical suppliers. Suppliers for Ford and General Motors were selected based on recent publicized awards recognition. If a supplier was

Table 1 Summary of responses DaimlerChrysler Number Sent Non-deliverable Ineligible Sample Size Respondents 171 25 1 145 32 Ford 67 6 1 60 22 General Motors 91 6 1 84 32 Honda 331 47 2 282 53 Total 660 84 5 571 139

Table 2 Average of the reported years of business with the buying firm Daimler­ Chrysler Means (years) 30 Ford 37 General Motors 31 Honda 14a Average 25

a Bonferroni difference test showed statistically significant differences between the mean for Honda and the three other means (P < 0.001).

listed for multiple manufacturers, the supplier was randomly retained for either Ford or General Motors because there were fewer potential respondents for those two firms. The unit of analysis was the business unit and the targeted respondents were the chief executive officers. Thus, of the targeted 660 automobile suppliers, 139 usable surveys were received for a response rate of 24.3% (139/571). Table 1 provides a summary of the respondents. Due to the differences in sampling frames, there was a potential selection bias. We attempted to control for selection bias by measuring for differences between the four supplier groups. Only one statistically significant difference was found; since Honda is a relatively recent entrant into the US market, the average lengths of Honda suppliers' relationships with their manufacturer were statistically less than average for the other manufacturers (P < 0.001), as reflected in Table 2.3 Statistically significant differences were not found between the supplier groups on the following: (1) the percentage of the supplier's business to

3 Due to the sample size of Honda suppliers (n = 53), a multi-group SEM comparison was not a viable option. Although it was not hypothesized, we inserted a dummy variable representing Honda suppliers as an influencing variable of buying firm's commitment or BSR. The model results did not change, indicating that the model is robust. The t-statistics were significant, which indicates that the age of the relationship should be considered in future models as a possible influencing variable.

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all automotive manufacturers, (2) annual gross sales dollars, (3) supplier's main business for the buying firm and (4) respondents' position titles. Attempts were made to minimize non-response through frequent and easy-to-understand correspondence (Dillman, 2000). Evidence of non-response bias was assessed by comparing answers between questionnaires that were returned early and those returned late to determine if there were statistical differences (Lambert and Harrington, 1990; Lessler and Kalsbeek, 1992). Specifically, the sample was split into two groups based on if the surveys were returned before or after the mailing of the second survey package. Twelve of the sixty survey items (20%) were randomly selected and t-tests were performed on each item (n1 = 72, n2 = 68). The t-tests yielded no statistically significant differences. Prior to the analysis, the data characteristics were examined. The variables, scales, associated factors, and descriptive statistics are shown in Table 3. Eleven surveys were missing data for the independent variables in the initial measurement model. Multiple linear regression was used to impute 15 missing completely-at-random items (0.24%) associated with the independent variables.4 One returned survey was excluded due to a lack of dependent variable data (as recommended by Hair et al., 1998). 4.4. The measurement model Using the two-step approach proposed by Anderson and Gerbing (1988), the first step was to purify and test the measurement model. A systematic, iterative process was used to determine which items should be eliminated from the measurement model. Please refer to Table 4 for the final factor loadings. Item elimination was based on weak loadings, cross loadings, multiple loadings, communalities, error residuals and theoretical determination.5 Since the suggested elimThe dependent variable was calculated by linearly regressing all of the items that were hypothesized within the same construct (Lessler and Kalsbeek, 1992). We also tested the final structural model using the most conservative method, listwise deletion (n = 135). We found no statistically significant differences between the results of the two sets of data although test statistics were slightly improved with listwise deletion. 5 Items S2, B2, I1, I2 and I3 had low loadings on all constructs. Items B6 and L1 loaded on indirect influence strategy. Item L2

4

ination of L1 and L2 would leave only two items to represent the operational linkages construct, the construct was eliminated (Ding et al., 1995). Of the 44 initial scale items, 28 items were retained during the measurement purification process. Each retained item was a statistically significant indicator of its respective construct.6 The fit indices in Table 5 indicated acceptable fit for the purified measurement model. 4.5. Validity of measurement model The validation process for the survey instrument had three steps: content validity, which we covered in designing the survey instrument; construct validity, which includes reliability; and nomological validity (O'Leary-Kelly and Vokurka, 1998). The literature review and in-depth interviews conducted with business executives and researchers established the basis of content validity of the survey instrument. The purpose of construct validity is to show that the items measure and are correlated with what they purport to measure, and that the items do not correlate with other constructs. Unidimensionality was established with target rotation factor analysis, where 0.30 was considered to be the lowest significant factor loading to define the construct (Guadagnoli and Velicer, 1988; Hair et al., 1998). Cronbach's alpha and alpha-if-item-deleted were calculated to determine construct reliability. As shown in Table 4, all Cronbach's alphas were above 0.70, where 0.70 is the suggested cutoff for established scales (Carmines and Zeller, 1979). Although the alpha-if-item-deleted indicated that three items (C5, FM4 and P3) had limited contribution to the Cronbach's alpha, each of the items was retained due to their significance in defining the theoretical construct.

loaded on cooperation. Item C1 loaded on buying firm's commitment. Item C3 loaded on supplier's commitment. Items C2 and C4 loaded on both buying firm's commitment and cooperation. Item S6 loaded on both operational linkages and indirect influence strategy. Item FB3 loaded on operational linkages, indirect influence strategy and feedback. 6 FM1 and FM4 were retained for theoretical reasons. FM1 represents this study's definition of formality and FM4, a reverse-coded item, represents one end of the formality continuum, word-of-mouth.

Table 3 Factors, variables, scales and descriptive statistics Factor Variable and survey questiona Scale activities within the past year to increase the = always to 7 = never = always to 7 = never = always to 7 = never = always to 7 = never = always to 7 = never = always to 7 = never 2.70 3.11 3.32 3.71 3.41 3.88 2.77 1.56 C. Prahinski, W.C. Benton / Journal of Operations Management 22 (2004) 39­62 1.85 1.84 1.63 1.62 1.74 1.31 Mean S.D.

Indirect influence strategy

Indicate the extent to which Mfg has engaged in each of the following performance or capabilities of your business: I1*: Assessment of your firm's performance through formal evaluation, 1 using guidelines and procedures I2*: Use of a supplier certification program to certify your firm's 1 process control I3*: Public recognition of your firm's achievements/performance 1 I4: Site visits by Mfg to your premises to help your firm improve 1 its performance I5: Inviting your personnel to Mfg 's site to increase your 1 awareness of how the product is used I6: Training and education of your personnel 1 FM1: In coordinating our activities with Mfg , formal communication channels are followed (i.e., channels that are regularized, structured modes versus casual, informal, word-of-mouth modes) FM2: Mfg has a formal system to track the performance of their suppliers FM3: Mfg has a formal program for evaluating and recognizing suppliers FM4: The source of our information about Mfg 's evaluation program is predominantly word-of-mouth. (R) FM5: Mfg 's evaluation process is conducted through standard procedures Our firm can easily approach Mfg for discussion: FB1: To clarify their expectations of our firm's performance FB2: Regarding their evaluation of our firm's performance FB3*: Regarding ideas for performance improvement. FB4: To establish goal activities for performance improvement B1: Mfg is loyal to our firm B2*: Mfg is continually on the lookout to reduce dependence on our firm. (R) B3: Mfg expects to buy our products for a long time B4: Mfg see this relationship as a long-term partnership B5: How strong is Mfg 's commitment with your firm? B6*: How significant are Mfg 's investments in your firm as compared to investments provided by other customers?

Formality

1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly Agree to 7 = strongly disagree

1.94 2.01 2.89 2.63

1.23 1.30 1.56 1.38

Feedback

1 1 1 1

= strongly = strongly = strongly = strongly

agree agree agree agree

to to to to

7 7 7 7

= strongly = strongly = strongly = strongly

disagree disagree disagree disagree

2.35 2.47 2.61 2.57 3.20 3.40 2.66 2.79 3.01 3.60

1.30 1.36 1.43 1.42 1.70 1.58 1.40 1.63 1.59 1.38

Buying firm's commitment

1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 1 1 1 7 = strongly agree to 7 = strongly disagree = strongly agree to 7 = strongly disagree = very strong to 7 = very weak = significantly higher to = significantly lower

49

50

Table 3 (Continued ) Factor Cooperation Variable and survey questiona C1*: Mfg is concerned about our firm's success C2*: Mfg will not take advantage of a strong bargaining position C3*: Mfg and our firm must work together to achieve our mutual goals C4*: Our relationship with Mfg is better described as a cooperative effort rather than an adversarial effort C5: When our firm has a problem, Mfg helps us solve it C6: How flexible is Mfg in response to requests your firm makes? C7: When we are solving problems jointly, how flexible is Mfg in resolving them? L1*: L2*: for L3*: L4*: links Our business activities are closely linked with Mfg We feel like we never know what we are supposed to be doing Mfg . (R) Our activities with Mfg are well coordinated We have a routine and well-established system that facilitates the of our operations with Mfg 's operations Scale 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = always to 7 = never 1 = very flexible to 7 = very inflexible 1 = very flexible to 7 = very inflexible 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = strongly agree to 7 = strongly disagree 1 = very strong to 7 = very weak 1 = significantly higher to 7 = significantly lower to to to to to to 7 7 7 7 7 7 = strongly = strongly = strongly = strongly = strongly = strongly disagree disagree disagree disagree disagree disagree Mean 3.55 4.91 1.80 2.68 3.19 3.57 3.26 2.47 2.76 2.86 2.60 1.66 3.66 2.03 1.94 1.60 2.61 S.D. 1.89 1.71 0.97 1.32 1.48 1.38 1.32 1.29 1.52 1.40 1.40 1.00 1.85 1.40 1.38 0.96 1.39

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Operational linkages

Supplier's commitment

S1: Our firm is loyal to Mfg S2*: Our firm is continually on the lookout to reduce dependence on Mfg as a customer. (R) S3: Our firm expects Mfg to buy our products for a long time S4: Our firm sees this relationship as a long-term partnership S5: How strong is your firm's commitment with Mfg ? S6*: How significant are your firm's investments in equipment dedicated to Mfg as compared to investments dedicated to other customers?

Supplier's performance

Compared to your competitors, how well does your firm perform on the following aspects? P1: Product quality (R) 1 = strongly agree P2: Delivery performance (R) 1 = strongly agree P3: Price (R) 1 = strongly agree P4: Responsiveness to requests for changes (R) 1 = strongly agree P5: Service support (R) 1 = strongly agree P6: Overall performance (R) 1 = strongly agree

2.32 2.37 3.36 2.40 2.18 2.28

1.25 1.31 1.32 1.31 1.17 1.08

a

Variables denoted with an asterisk () were subsequently dropped from the study. Variables denoted with an (R) were reverse coded for all analyses.

C. Prahinski, W.C. Benton / Journal of Operations Management 22 (2004) 39­62 Table 4 Final factor loadings

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Note: Questions FM4 and P(1­6) were reverse coded for all analyses.

The third step of construct validity is to establish convergent and discriminant validity. Convergent validity was supported with all t-values greater than 2.0 (Pedhazur and Schmelkin, 1991). Discriminant validity was assessed by calculating the 95% confidence interval from the data in Table 6 by adding and subtracting twice the standard error of a correlation between two latent variables (Anderson and Gerbing, 1988). 4.6. Validity of second-order factors Higher-order factors are determined by factoring the correlations among the first-order factors. The ma-

trices can be determined in the same manner as the first-order factors, with two main differences. First, it should be noted that higher order factors reduce accuracy as a tradeoff for an increase in the breadth of generalization. Second, significance tests are not applicable because the distribution characteristics of the correlation coefficients are a function of the rotation procedure of the first-order factors and sample size (Gorsuch, 1983). Based on theoretical justification described in the literature review section, it was anticipated that up to two second-order factors exist within the four models. Using the correlations among the seven first-order

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Table 5 Measures of model fit and statistical power Desirable range 2 test statistic 2 /d.f. Non-normed fit index Bollen's 1989 fit index Relative non-centrality index Comparative fit index RMSEA RMSEA confidence interval Degrees of freedom Effective number of parameters Power at = 0.05 and alternate RMSEA = 0.08 Measurement model 606 1.84 0.89 0.91 0.91 0.91 0.078 0.068, 0.088 329 77 >0.99 Indirect influence strategy model 384 2.34 0.87 0.89 0.89 0.89 0.099 0.086, 0.111 164 46 >0.99 Formality model 438 2.16 0.88 0.90 0.90 0.90 0.092 0.080, 0.103 203 50 >0.99 Feedback model 379 2.31 0.89 0.91 0.91 0.91 0.097 0.085, 0.110 164 46 >0.99 Collaborative communication model 630 1.85 0.89 0.90 0.90 0.90 0.078 0.069, 0.088 341 65 >0.99

3.0 0.90 0.90 0.90 0.90 0.08 for reasonable fit

C. Prahinski, W.C. Benton / Journal of Operations Management 22 (2004) 39­62 Table 6 Correlations among latent variables (lower triangle) and standard errors after rotation (upper triangle)

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factors, shown in the lower triangle of Table 6, the rotated matrix in Table 7 supports three factors: buyer­supplier relationship, collaborative communication, and performance. In general, the loadings support the theoretical second-order factor relationships. The hypothesized structural model required minor modification since the operational linkages construct was dropped from the measurement model. With only two first-order factors, BSR would be under-identified (Gorsuch, 1983; Byrne, 1994) unless the path coefficient for buying firm's commitment, 1 , and cooperation, 2 , were set equal to each other. Content and construct validities of the scales were supported. The next step to validate the scales is to assess nomological validity, which was supported with results of the hypothesis testing described in the next section.

5. Results Structural equation modeling (SEM) was utilized to simultaneously measure the hypothesized multiple linear relationships. As discussed in Section 4.4, the fit

for the revised model was deemed acceptable. Since second-order factors were incorporated into the structural model, the validity of the second-order factors was established and discussed in Section 4.6. Using Anderson and Gerbing's two-step approach (1988), the second step is to simultaneously test the hypothesized relationships among the factors using SEM. Figs. 2­5 represent the four models, each shown with their associated path coefficients and specific mathematical equations. As shown in Table 5, the fit indices of the structural model were similar for the first three models: indirect influence, formality and feedback. When compared to the cut-off criteria, most of the fit indices (e.g., 2 /d.f., NNFI, Bollen's 89, RNI, CFI and RMSEA) were consistent with the rule-of-thumb measures (Hair et al., 1998). The RMSEA fit statistic for the collaborative communication model had significantly improved fit as compared to the other models. Based on the fit and theoretical support, the structural models were deemed acceptable and revisions were not made. The results for the hypotheses testing for each of the four models are summarized in Table 8.

Table 7 Second-order factor loadings

Factor loadings greater than 1.0 (#) are possible due to the nature of the oblique rotation method.

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1 = 0.278

2 = 0.278

Buying Firm's Commitment 1 1 = 0.850*** 4 = 0.687 1 = 0.560*** Indirect Influence Strategy 1

Cooperation 2 2 = 0.850*** 4 = 0.836*** Buyer-Supplier Relationship 4 2 = -0.040 6 = 0.896 5 = -0.059

5 = 0.302 Supplier's Commitment 5 6 = 0.386** Supplier's Performance 6

Fig. 2. Indirect Influence Strategy Structural Equation Model and Equations. Solids paths indicate significant results, where: () indicates significance at P < 0.10, () indicates significance at P < 0.05; () indicates significance at P < 0.01; Dashed paths indicates non-significant results. 1 and 2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages, 3 , was dropped since the construct could not be validated. 1 = 0.8504 + 0.278, 2 = 0.8504 + 0.278, 4 = 0.5601 + 0.687, 5 = 0.8364 + 0.302, 6 = -0.0401 + (-0.0594 ) + 0.3865 + 0.896 where: i , is the endogenous variable, i; 1 , the exogenous variable, indirect influence strategy.

1 = 0.305

2 = 0.305

Buying Firm's Commitment 1 1 = 0.834*** 4 = 0.636 1 = 0.603*** Formality 2

Cooperation 2 2 = 0.834*** 4 = 0.868*** Buyer-Supplier Relationship 4 2 = -0.004 6 = 0.891 5 = -0.122

5 = 0.247 Supplier's Commitment 5 6 = 0.432* Supplier's Performance 6

Fig. 3. Formality Structural Equation Model and Equations. Solids paths indicate significant results, where () indicates significance at P < 0.10; () indicates significance at P < 0.05; () indicates significance at P < 0.01; Dashed paths indicates non-significant results. 1 and 2 were set equal to each other so that the second-order factor was fully identified. Operational linkages, 3 , was dropped since the construct could not be validated. 1 = 0.8344 + 0.305, 2 = 0.8344 + 0.305, 4 = 0.6032 + 0.636, 5 = 0.8684 + 0.247, 6 = -0.0042 + (-0.1224 ) + 0.4325 + 0.891 where: i , is the endogenous variable, i; 2 , the exogenous variable, formality.

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1 = 0.285

2 = 0.285

Buying Firm's Commitment 1 4 = 0.510 1 = 0.700*** Feedback 3

Cooperation 2 1 = 0.845*** 2 = 0.845*** 4 = 0.841*** Buyer-Supplier Relationship 4 2 = 0.068 6 = 0.887 5 = -0.158

5 = 0.293 Supplier's Commitment 5 6 = 0.420** Supplier's Performance 6

Fig. 4. Feedback Structural Equation Model and Equations. Solids paths indicate significant results, where () indicates significance at P < 0.10; () indicates significance at P < 0.05; () indicates significance at P < 0.01; Dashed paths indicates non-significant results. 1 and 2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages, 3 , was dropped since the construct could not be validated. 1 = 0.8454 + 0.285, 2 = 0.8454 + 0.285, 4 = 0.7003 + 0.510, 5 = 0.8414 + 0.293, 6 = 0.0683 + (-0.1584 ) + 0.4205 + 0.887 where: i , is the endogenous variable, i; 3 , the exogenous variable, feedback.

1 = 0.286

2 = 0.286

7 = 0.556

Buying Firm's Commitment 1 1 = 0.845*** 4 = 0.251

Cooperation 2 2 = 0.845*** 4 = 0.846*** Buyer-Supplier Relationship 4 2 = 0.067 6 = 0.887 5 = -0.179

5 = 0.284 Supplier's Commitment 5 6 = 0.430** Supplier's Performance 6

Indirect Influence Strategy 7 8 = 0.499 7 = 0.666*** 8 = 0.708***

1 = 0.866*** Formality 8 9 = 0.358 Feedback 9 Collaborative Communication 4

9 = 0.801***

Fig. 5. Collaborative Communication Structural Equation Model and Equations. Solids paths indicate significant results, where () indicates significance at P < 0.10; () indicates significance at P < 0.05; () indicates significance at P < 0.01; Dashed paths indicates non-significant results. 1 and 2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages, 3 , was dropped since the construct could not be validated. 1 = 0.8454 + 0.286, 2 = 0.8454 + 0.286, 4 = 0.8664 + 0.251, 5 = 0.8464 + 0.284, 6 = 0.0674 + (-0.1794 ) + 0.4305 + 0.887, 7 = 0.6664 + 0.556, 8 = 0.7084 + 0.499, 9 = 0.8014 + 0.358, where i , is the endogenous variable, i; 4 , the exogenous variable, collaborative communication.

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Table 8 Summary of test results for structural model Model Indirect influence strategy Hypothesis 1a 2a 3 4 5 1b 2b 3 4 5 1c 2c 3 4 5 1d 2d 3 4 5 Path 1 2 4 5 6 1 2 4 5 6 1 2 4 5 6 1 2 4 5 6 Influence BSR Influence performance BSR supplier's commitment BSR performance Supplier's commitment performance Formality BSR Formality performance BSR supplier's commitment BSR performance Supplier's commitment performance Feedback BSR Feedback performance BSR supplier's commitment BSR performance Supplier's commitment performance Collaborative BSR Collaborative performance BSR supplier's commitment BSR performance Supplier's commitment performance Path coefficient 0.560 -0.040 0.836 -0.059 0.386 0.603 -0.004 0.868 -0.122 0.432 0.700 0.068 0.841 -0.158 0.420 0.866 0.067 0.846 -0.179 0.430 R2 0.313 0.025 0.698 0.076 0.098 0.364 0.028 0.753 0.084 0.100 0.490 0.043 0.707 0.082 0.103 0.749 0.071 0.716 0.080 0.103 t-value 6.65 -0.30 18.25 -0.24 1.77 9.18 -0.04 20.53 -0.41 1.63 12.96 0.50 19.40 -0.57 1.86 16.94 0.23 20.31 -0.44 1.86 Hypothesis supported? Yes No Yes No Yes Yes No Yes No Yes Yes No Yes No Yes Yes No Yes No Yes

Formality

Feedback

Collaborative

Operations linkages was dropped since the construct could not be validated. BSR represents the construct "buyer­supplier relationship". Influence represents the construct "indirect influence strategy". Performance represents the construct "supplier's performance". Path significant at P < 0.10. Path significant at P < 0.05. Path significant at P < 0.01.

5.1. The effect of indirect influence strategy We hypothesized that indirect influence strategy expressed by the buying firm to the supplier positively influences the buyer­supplier relationship (H1a ). The results in Table 8 and Fig. 2 show that indirect influence strategy significantly influences the buyer­supplier relationship (P < 0.01), as indicated by the 1 path coefficient of 0.56. Buying firms that emphasize education, training and site visits enhance the supplier's perceptions of the buying firm's commitment and cooperation. It was hypothesized that indirect influence strategy expressed by the buying firm (source) to the supplier (target) positively influences the supplier's performance (H2a ). This hypothesis was not supported. The 2 path coefficient of -0.04 in Table 8 and Fig. 2 was not significantly different from zero (P > 0.10). Although the buying firm is attempting to educate and

train the supplier, suppliers do not perceive that the buying firm's attempts directly enhance the supplier's performance. 5.2. The effect of formality In the formality model shown in Fig. 3, the hypothesis H1b is supported; communication formality established between the buying firm and the supplier positively influences the buyer­supplier relationship. When the buying firm utilizes a formal system for evaluations, the supplier's perceptions of the buyer­supplier relationship are enhanced. In Table 8, 1 path coefficient of 0.60 is statistically significant (P < 0.01). We hypothesized that communication formality would directly influence the supplier's performance (H2b ). This hypothesis was not supported. The 2 path coefficient of -0.004 shown in Table 8 and Fig. 3 was

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not significantly different from zero (P > 0.10). Although the buying firm is attempting to use formal systems of communicating the supplier evaluation, there is not a direct influence on the supplier's performance. 5.3. The effect of feedback In the feedback model, we hypothesized that communication feedback between the buying firm and the supplier positively influences the buyer­supplier relationship (H1c ). With the statistically significant 1 path coefficient of 0.70, the results shown in Table 8 and Fig. 4 indicate that the hypothesisis strongly supported (P < 0.01). When the buying firm establishes an environment conducive to open dialogue regarding the supplier evaluation, the supplier perceives that the buying firm is committed to the relationship and that the buying firm will cooperate in solving problems. The hypothesis H2c that communication feedback between the buying firm and the supplier positively influences the supplier's performance was not supported. The 2 path coefficient of 0.068 was not significantly different from zero (P > 0.10). The results of the study show that supplier's perceptions of improvements in understanding gained through feedback of the supplier's performance evaluation do not directly impact the supplier's performance. 5.4. The effect of collaborative communication We hypothesized that collaborative communication between the buying firm and the supplier positively influences the buyer­supplier relationship. With the statistically significant 1 path coefficient of 0.87, the results shown in Table 8 and Fig. 5 indicate that respondents perceive collaborative communication to positively influence supplier's perceptions of the buyer­supplier relationship; H1d is strongly supported (P < 0.01). When the buying firm uses indirect influence strategy, formality and feedback, in unison, for their supplier development program, the suppliers' perceive an improvement in the buyer­supplier relationship. The disturbance variable, 4 , includes the variables that influence buyer­supplier relationship but are excluded from the 4 equation (Bollen, 1989). In the collaborative communication model shown in Fig. 5, 4 of 0.25 is significantly less than the 4 of the other

models (0.69, 0.64, and 0.51 in the indirect influence strategy, formality and feedback model, respectively.) As shown in Table 8, the path 1 in the collaborative communication model accounts for approximately 75 per cent of the variance in the BSR construct, as indicated by the R2 . Respondents indicate that the buying firm's collaborative communication explains a significant amount of the suppliers' perceptions of the buyer­supplier relationship. Hypothesis 2d, that collaborative communication between the buying firm and the supplier positively influences the supplier's performance, was not supported. The 2 path coefficient of 0.067 was not significantly different from zero (P > 0.10). Respondents indicated that the buying firm's collaborative communication does not directly influence the supplier's performance. 5.5. The influence of buyer­supplier relationship on supplier's commitment It was hypothesized that the buyer­supplier relationship positively influences the supplier's commitment to the buying firm (H3 ). The statistically significant 4 path coefficients (P < 0.01) in Table 8 of 0.84, 0.87, 0.84 and 0.85 for the indirect influence strategy, formality, feedback and collaborative communication models, respectively, indicate support for the hypothesis in all four models. Suppliers are committed to the buying firm when suppliers perceive that the buying firm is cooperative and committed to the supplier. Two measures of commitment were validated from the supplier's perspective: buying firm's commitment and supplier's commitment. 5.6. The influence of buyer­supplier relationship on supplier's performance It was hypothesized that the buyer­supplier relationship positively influences the supplier's performance (H4 ). For each model in Table 8, the 5 path coefficients of -0.06, -0.12, -0.16 and -0.18 in the indirect influence strategy, formality, feedback and collaborative communication models, respectively, are not significantly different from zero. Therefore, H4 was not supported for any of the four models. The respondents do not perceive that the buyer­supplier relationship directly influences supplier's performance.

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5.7. The influence of supplier's commitment on supplier's performance We hypothesized that the supplier's commitment to the buying firm positively influences the supplier's performance (H5 ). The 6 paths of 0.39, 0.43, 0.42, and 0.43 for the indirect influence strategy, formality, feedback and collaborative communication model, were statistically significant (P < 0.05) as shown in Table 8. Therefore, the data indicates that there is support for H5 . When the supplier has a higher level of commitment to a buying firm, the supplier perceives that they have a higher level of performance associated with the buying firm. 5.8. Power analysis High statistical power in research improves the probability of repeatability of the study results. Power is the probability of correctly rejecting the null hypothesis when it should be rejected. A post hoc power analysis was conducted for each of the four communication models using STATISTICA software. The power of each of the models was very near 1.00, as reflected in Table 5, and therefore, the decisions that indicated `support' for the hypotheses would appear to have a high probability of being correct.

The buying firm's underlying objective of the SDP is to enhance the supplier's performance (Handfield et al., 2000; Krause, 1997; Watts and Hahn, 1993). Krause et al. (2000) found that buying firm's perceptions of direct involvement with SDP influenced the firm's performance. The results of the current study suggest that the supplier may perceive the effect of indirect influence strategy differently than the buying firm. The supplier does not perceive that the buying firm's indirect influence strategy directly affects the supplier's performance. In general, when the buying firm establishes education and training programs, they should not expect that the supplier's performance and capabilities would improve, except when the intervening BSR and supplier's commitment are positively affected. 6.2. The effect of formality This research is the first to illustrate the importance of the formal communication structure from the supplier's perspective. The results are consistent with the existing literature from the buying firm's perspective (Carr and Pearson, 1999). Executives at supplying firms positively perceive the standardized procedures and formal channels of communicating the supplier evaluations. However, the buying firm cannot simply establish SDPs, such as a formal evaluation program, and expect that the supplier's performance and capabilities will improve. An improved supplier's performance requires the coordination of factors that are outside of the buying firm's realm of control. The buying firm needs to establish an environment that is conducive to improving the controllable factors, such as the buyer­supplier relationship. 6.3. The effect of feedback

6. Discussion and managerial implications The purpose of this research was to determine the supplier's perceptions of the buying firm's supplier evaluation communication strategies and the influence of the communication on supplier's performance. Based on the results, several key insights emerge. 6.1. The effect of indirect influence communication strategy Executives at buying firms who want to cultivate improved relationships with their suppliers should consider site visits, education and training programs targeted to the supplier's personnel. The focus of these programs should be on production and process techniques that impact the supplier's output and future capabilities.

The impact of feedback on the buyer­supplier relationship is addressed for the first time in this study. For an enhanced buyer­supplier relationship, buying firm executives need to listen to their suppliers' suggestions for performance improvement and to clarify the buying firm's objectives, evaluation procedures and evaluation results. This feedback opportunity enhances the supplier's perceptions of the buying firm's cooperation and commitment to the supplier.

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Although the supplier may be more receptive to suggestions and may become more committed to the buying firm, the bottom line is not directly impacted by feedback opportunities. Since effective communication of the buying firm's objectives aids the supplier's understanding, the feedback mechanisms should be utilized in a similar manner as the indirect influence strategy, i.e., a mechanism to increase the education and training of the supplier's personnel. 6.4. The effect of collaborative communication Collaborative communication positively influenced buyer­supplier relationship, which influenced buying firm's commitment and cooperation. When implementing SDPs, executives at buying firms should use indirect influence strategy, formal program structure and feedback with their most critical suppliers. Each of these three communication strategies used together are more powerful in their influence of buyer­supplier relationship than any one strategy used in isolation. Successfully implemented collaborative communication of the SDP effort significantly and positively influences the supplier's perceptions of the buyer­supplier relationship. This finding is consistent with the Mohr et al. study (1996). This study also assessed the supplier's perceptions of the buying firm's collaborative communication influence on suppliers' performance. However, as with the other communication strategy efforts, data do not support the hypothesis that collaborative communication is positively associated with suppliers' performance. The buying firm manager should not anticipate improved supplier performance as a result of the supplier development communication effort. 6.5. The influence of buyer­supplier relationship on supplier's commitment When suppliers perceive that executives at buying firms emphasize improved buyer­supplier relationships, the suppliers' commitment to the buying firm will increase. Since businesses are increasingly reducing their supplier base and becoming more dependent on their suppliers, buying firm managers are increasingly vulnerable to the supplier's whims. To counter this risk, buying firm managers need their suppliers to also become more committed to the relation-

ship. Without the supplier's commitment, the buying firm may be unable to meet their business objectives. This commitment could easily become a competitive advantage for successful buying firms in an oligopolistic environment. Both the buying firm's commitment and the supplier's commitment were composed of items that measured these three dimensions: loyalty, willingness to make investments and expectations of relationship continuity. The final measurement model, however, excluded the willingness to make investment in the trading partner's business, a measure that Williamson (1985) supported with his transactional cost economics. 6.6. The influence of buyer­supplier relationship on performance Prior studies that utilized a one-dimensional buyer­supplier relationship construct and/or considered the buying firm's perspective (Carr and Pearson, 1999; Shin et al., 2000; Vijayasarathy and Robey, 1997) found that buyer­supplier relationship influenced performance. The results of this study appear to contradict findings from the buying firm's vantage point. Two possible implications of this result are that the complexity of the buyer­supplier relationship construct should not be ignored, and that the buying firm's perspective could be distinctly different from the supplier's perspective. The managerial implications of this result are that `good relations' with suppliers do not directly influence the suppliers' performance. The buying firm's cooperative efforts and expression of commitment do not directly translate into better product quality, delivery performance, price, responsiveness, service, and overall performance from the supplier. However, as will be further discussed in Section 6.7, the results do not indicate that managers should be unconcerned with the development of good business relationships. 6.7. The influence of supplier's commitment on performance This research provides empirical support for the proposition presented in Lambert et al. (1996) that when a business considers a firm to be critical for its success, managers should focus on improving the

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relationship with that firm. Lambert et al. (1996) postulated that an improved relationship enhances business performance for both firms. The current study found that when managers focus on improving the relationship, such as with increased cooperation and commitment, the supplier's commitment is enhanced (H3 ), which, in turn, improves the supplier's performance (H5 ). The managerial significance of this result is that SDPs are effective when the supplier is committed to the buying firm. If the supplier is not committed, the buying firm cannot influence the supplier's performance through the supplier evaluation communication process. The buying firm needs to influence the supplier's commitment in order to positively influence the supplier's performance.

7. Conclusions When the buying firm uses collaborative communication for the supplier development programs, it is perceived by the supplier as an effective mechanism to improve the buyer­supplier relationship. Collaborative communication includes indirect influence strategy, formality and feedback. However, this study shows that the implementation of several supplier evaluation communication strategies by itself is not enough to influence the supplier's performance. Although the buying firm currently considers their commitment and the importance of the supplier's product and/or service to the supplier when initiating their targeted supplier development programs (Krause et al., 1998), the buying firm may want to also consider the supplier's perspective of the relationship prior to initiating supplier development programs. Supplier development programs will be successful in terms of operational performance measures if the supplier is committed to the buying firm. As noted by a buying firm manager in Porter (1991), "Are we the customer of choice with our suppliers?" The supplier evaluation communication process could be the catalyst that strengthens the buyer­supplier relationship and supplier's commitment. There appears to be an explanation for why some suppliers have not adequately improved their performance to meet the buying organization's SDP

initiatives. The supplier must feel a strong sense of commitment, loyalty, and longevity in the relationship with the buying firm. The buying firm can influence the supplier's commitment through enhanced communication and relationship development. Relationship development includes enhancing cooperation, problem solving, and expressing their commitment, loyalty and desire to continue the relationship for many years into the future. Several implications for business managers can be drawn from this research. For the buying firm manager, specific communication strategies should be designed into their SDP efforts. The program should be formalized with routine communication; incorporate supplier training, education and site visits to aid in the learning process; and provide opportunities for feedback to clarify program objectives and improvement suggestions. The result of the SDP collaborative communication effort should enhance supplier's perceptions of the business relationship and their commitment to the buying firm. Buying firm managers should focus their SDP implementation efforts on suppliers that exhibit commitment to the buying firm. Although the buying firm's perceptions of the supplier's commitment are inherently biased, it represents the best proxy for the supplier's commitment. As the recipient of their customer's SDP efforts, the supply firm manager has the opportunity to improve the relationship with the customer. Improved relationships can result in increased market share, growth opportunities and other benefits. In addition, when SDPs are implemented, the supply firm can take advantage of the learning opportunities and improve its overall performance with the buying firm and with their other customers. This research has explored a relatively new area of supply chain communication and supplier development programs. Insights from this study have implications in the marketing area for specific channel conditions, such as structure, climate and power. Further theoretical work could expand the model by including other dimensions of communication strategy. This research incorporated indirect influence strategy, formality and feedback, as well as an encompassing construct, collaborative communication. Other communication dimension strategies include: direct influence strategy, informality (such

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