Investigating the antecedents and outcomes of customer firm transaction cost savings in a supply chain relationship

Journal of Business Research 59 (2006) 62 – 72

Investigating the antecedents and outcomes of customer firm transaction cost savings in a supply chain relationship Neeraj Bharadwaja,*, Ken Matsunob,1 a

McCombs School of Business, The University of Texas at Austin, CBA 7.256, Mailcode: B6700, Austin, TX 78712, United States b The Irving Pike Term Chair, Babson College, 209 Malloy Hall, Babson Park, MA 02457-0310, United States Received 17 March 2004; accepted 4 March 2005

Abstract Cost reduction has become a preeminent goal for businesses. Since firms spend a significant portion of their annual revenues on the acquisition of an array of goods and services from suppliers, organizational procurement has been identified as an area holding tremendous potential for the removal of nonvalue-adding costs. This effort examines how a vendor’s order management cycle performance and trust can affect a customer firm’s transaction costs, which in turn, affect such customer-related outcomes as customer satisfaction and future purchase intentions. The results are theoretically meaningful as they address gaps identified in previous writings and pragmatically useful as they offer managers practical insight into important bases for securing a competitive advantage. D 2005 Elsevier Inc. All rights reserved. Keywords: Order management cycle performance; Transaction costs; Trust; Customer satisfaction; Future intentions; Buyer – supplier relationships

1. Introduction Cost reduction has become a preeminent goal for businesses (Denison, 2003). As a result, firms are ‘‘seeking ways to minimize overhead costs, to eliminate intermediate production steps, to reduce transaction and other ‘‘friction’’ costs, and to optimize business processes across functional and organizational boundaries’’ (Treacy and Wiersma, 1993, p. 85). Since 30 –70% of a firm’s annual revenues are expended on acquiring an array of goods and services (Killen and Kamauff, 1995), firms are pursuing such initiatives as enterprise resource planning (Trent and Monczka, 1998), just-in-time sourcing (Frazier et al., 1988), electronic catalogs (Pierson, 2002), reverse auctions (Jap, 2002), and global sourcing (Venkatraman, 2004) in an attempt to remove nonvalue-adding costs from the in-bound supply chain. * Corresponding author. Tel.: +1 512 471 8312; fax: +1 512 471 1034. E-mail addresses: [email protected] (N. Bharadwaj), [email protected] (K. Matsuno). 1 Tel.: +1 781 239 4363. 0148-2963/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2005.03.007

While research in marketing has been preoccupied with investigating factors that can affect top-line revenues, relatively little attention has been devoted to understanding how managing the middle line (i.e., cost of goods sold) can also contribute to driving the bottom line. Since reducing costs in the inbound supply chain is yet another means to enhance cash flows (Srivastava et al., 1999), this research examines issues pertaining to customer firm transaction costs within an industrial purchasing context. More specifically, the main research questions investigated include: (1) what factors can influence customer firm transaction cost savings in a buyer – supplier relationship? and (2) what is the effect of customer firm transaction cost advantage on customer satisfaction and future purchase intentions? The answers to these questions are important for both theory and practice. Regarding the former, academics have called for examination into the factors affecting firm transaction costs (Rindfleisch and Heide, 1997; Dahlstrom and Nygaard, 1999) as well as into the relationship between customer firm transaction costs and future intentions (Cannon and Homburg, 2001). This article extends current marketing knowledge by taking a novel approach to explore

N. Bharadwaj, K. Matsuno / Journal of Business Research 59 (2006) 62 – 72

how a seller’s performance along the order management cycle as well as trust can influence customer firm transaction costs, which in turn, affect such customer-related outcomes as customer satisfaction and future purchase intentions. Given that firms devote significant resources to procurement and that transaction costs often exceed actual invoice costs (Noordewier et al., 1990), this research also provides managers with insight into the factors that can introduce nonvalue-added costs into their firm’s procurement activity.

2. Conceptual framework and hypotheses The theoretical model that guides this research appears in Fig. 1. Since the organizational buying literature stresses the importance of identifying the key buying criteria that a customer firm uses to select and evaluate vendors (Lehmann and O’Shaughnessy, 1974, 1982; Wilson, 1994), the ensuing discussion commences with a description of order management cycle performance and then elaborates upon the hypothesized theoretical relationships depicted below. 2.1. Order Management Cycle (OMC) As products offered by industrial suppliers become increasingly commoditized, some authors prescribe that selling firms ought to take a more holistic view of their need-satisfying offering, and look to the ‘‘augmented’’ product as a means of differentiation (Corey, 1975; Levitt, 1980; Rangan and Bowman, 1992). Shapiro et al. (1992) advance that one means by which to move beyond the product is to explore the ‘‘order management cycle’’ (OMC), which refers to the critical activity sequence that a customer order follows from the time that the customer firm has placed an order through post-sales assistance. Drawing upon the OMC, we view the supplier’s order fulfillment, billing, and post-sales service as constituting the critical operational factors. These include: order cycle time, accuracy in filling orders, accuracy in billing processes, ontime delivery performance, ability to fill emergency orders, condition of products on arrival, and post-sales assistance (e.g., installation, training, and complaint resolution). These operational factors are tangible and measurable criteria for which metrics can be established for improving performance (Day, 1994). Given the ability of the OMC to impact critical

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operational metrics, Shapiro et al. (1992) advance that ‘‘focusing on the OMC offers managers the greatest opportunity to improve overall operations and create new competitive advantages’’ (p. 113). 2.2. Trust Social Exchange Theory (SET) suggests that a firm will remain in an exchange as long as the benefits provided by a vendor outweigh those provided by alternative sources (Thibaut and Kelley, 1959; Frazier, 1983). SET predicts that if a vendor can outperform the others in a buying firm’s consideration set, the buying firm is more likely to develop a favorable attitude towards the vendor. In the context of a supply chain relationship, a vendor’s superior OMC performance over other vendors may lead to trust, which is a favorable attitude that exists ‘‘when one party has confidence in an exchange partner’s reliability and integrity’’ (Morgan and Hunt, 1994, p. 23). In fact, how trust emanates from a supplier’s OMC performance can be explained well by how recurrent transactions allow the firm to gain confidence in the vendor. For one, a vendor that has been able to repeatedly meet the buyer’s requirements along the OMC will be perceived as one that has delivered on its promises, both implicit and explicit. Since past research has demonstrated that a vendor will be deemed a reliable exchange partner if it is capable of demonstrating technical competence (Moorman et al., 1993; Narayandas and Rangan, 2004), it can be argued that a vendor that delivers products on time and in good condition, provides invoices that are accurate, and is able to reconcile issues through its post-sales assistance activity will be able to convey its reliability along the OMC. Second, the customer firm will be able to develop a better sense of its exchange partner’s motives over recurrent transactions. Through these repeated interactions, the buyer will be able to secure the additional data points needed to more accurately discern the exchange partner’s goals and objectives, and assess the vendor’s likelihood of behaving opportunistically (Williamson, 1985; Doney and Cannon, 1997). Since it is difficult to know a priori which suppliers are trustworthy and which are not without direct, first-hand experience (Barney, 1990), the buying organization is likely to rely on its evaluation of the historical performance of its vendor along the OMC. This performance-based evaluation will enable the customer firm to gauge the vendor’s H4

Order Management Cycle Performance

Trust

H1

H2

Customer Future Intentions

Customer Firm Transaction Cost Advantage

H3 Fig. 1. Theoretical model.

H6 H5

Customer Satisfaction

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N. Bharadwaj, K. Matsuno / Journal of Business Research 59 (2006) 62 – 72

integrity, leading to form a level of trust commensurate to the vendor’s track record. Thus, the better the seller’s past OMC performance, the more likely the customer firm will have faith in the vendor’s reliability and integrity. H1. The customer firm’s assessment of a supplier’s order management cycle performance is positively related to buying firm trust in the supplier. 2.3. Transaction cost advantage If a customer firm believes that a given vendor is reliable and is not likely to act opportunistically, behavioral uncertainty surrounding the other party will be lowered. Lower levels of behavioral uncertainty ought to decrease the costs associated with having to monitor and evaluate the performance of the exchange partner (Rindfleisch and Heide, 1997). Conversely, higher levels of behavioral uncertainty will lead the customer firm to more ‘‘carefully scrutinize and monitor the other partner’s behavior, to guard against the partner’s opportunism, and to incur various costs of such vigilance’’ (Geyskens et al., 1996, p. 308). In the language of Transaction Cost theory (Williamson, 1975, 1985), the presence of trust ought to reduce transaction costs as the trust that has developed over recurrent transactions will reduce the cost of evaluating and selecting exchange partner(s) for every transaction occasion. Drawing upon an existing conceptualization of transaction costs in a buyer – supplier exchange (Noordewier et al., 1990), it is advanced that trust will lower a customer firm’s acquisition as well as possession costs. As far as the former is concerned, the focal buying firm will face lower acquisition costs in that it will have to devote fewer resources towards verifying orders, reconciling billing statements and invoices, and following-up with a vendor due to deviations from previously agreed upon tolerances (Cannon and Homburg, 2001; Dahlstrom and Nygaard, 1999). In addition, the focal firm will be subjected to lower possession costs as there is less likely to be a temporal discrepancy between when the item is ordered and the time that it is utilized (Noordewier et al., 1990). This will lower expenses associated with having to maintain storage areas, carry inventory, and pay insurance premiums to offset such risks associated with carrying inventory as pilferage and obsolescence. As a result, transaction costs should be lowered because the focal firm has trust in the vendor. Thus, buying firm trust in a supplier should minimize the sum of the acquisition and possession costs, thereby providing the buying firm with a perceived transaction cost savings (referred to as transaction cost advantage from here on).

transaction cost advantage without necessarily involving the formation of trust. Since trust is a favorable attitude pertaining to a vendor’s reliability and integrity and is formed in the context of competitive (and comparative) evaluations of alternative vendors over repeated transactions, there may be instances in which multiple vendors are equally capable of attending to the order management cycle, resulting in no clear winner of trust from the focal buying firm. Under such a scenario, a supplier’s operational performance may directly and positively influence the customer firm’s transaction costs. Thus, the possibility exists that the vendor’s OMC performance can lead directly to a reduction in acquisition and possession costs. For instance, if a supplier has been able to reduce the order cycle time from an average of 40 days to 20 days and/or improve customer fill rates from 90% to 95% and/or improve billing accuracy from 75% to 85%, the buying firm may have experienced savings from not having to deploy resources to stringently evaluate and monitor its exchange partner. In addition to realizing savings from not having to scrutinize the vendor, the customer firm will not have to spend unnecessary time and costs to seek out and qualify new vendors. Thus, it is reasonable to hypothesize that a vendor that performs well along the critical operational metrics can directly yield a customer firm transaction cost advantage for the buying firm. H3. The customer firm’s assessment of a supplier’s order management cycle performance is positively related to customer firm transaction cost advantage. 2.4. Customer future intentions Transaction cost advantage, in turn, can drive future intentions either directly or indirectly. With regard to the former, Cannon and Homburg (2001) found support for the direct relationship between customer firm procurement cost savings and a firm’s desire to expand future business with a vendor. Academics working from the means-end paradigm, however, would suggest that satisfaction mediates the transaction cost advantage—future intention relationship (e.g., Woodruff and Gardial, 1996). That is, the extent to which a customer firm is able to derive benefits from the exchange that enable it to realize its higher order goals will influence satisfaction, which in turn, will affect its decision to continue the relationship. Thus, H4. The transaction cost advantage that the customer firm derives by working with a supplier is positively related to its future intentions with the vendor.

H2. Buying firm trust in the supplier is positively related to the transaction cost advantage that the customer firm derives by working with a supplier.

H5. The transaction cost advantage that the customer firm derives by working with the supplier is positively related to its satisfaction with the vendor.

On the other hand, it is also possible that a vendor’s order management cycle performance can directly influence

H6. The customer firm’s satisfaction with a vendor is positively related to its future intentions with the vendor.

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3. Method 3.1. Sample and data collection The unit of analysis for this research is a specific manufacturer – supplier relationship. For investigating dyadic relational exchanges, data can be collected from the supplier’s perspective, the customer firm’s perspective, or both. For this study, data were obtained from the vantage point of the buying organization as researchers are increasingly calling for examining phenomenon from the perspective of the buyer (Cannon and Perreault, 1999; Day, 1994). The central rationale for exploring phenomenon from the customer’s vantage point is that the buyer is the ultimate arbiter as to whether to carry on with a given vendor, and is in the best position to convey whether it has been able to realize a transaction cost savings. The key informants for this study are procurement professionals. Given that past research has readily established that the purchasing function tends to be wellinformed about a firm’s relationship with its suppliers (Cannon and Perreault, 1999), a mailing list consisting of 2000 purchasing professionals employed in SIC Codes 35 and 36 (i.e., primarily computer and electronics manufacturers) was secured from the Institute for Supply Management (ISM). Each participant was mailed the following items: a cover letter on official university letterhead introducing our study, a letter from the CEO of ISM endorsing this research project, a seven page survey, and a self-addressed, stamped return envelope. They were asked to evaluate the supplier interface of their choice from which they secured a component part (i.e., a finished or nearly finished input that becomes a part of the buying organization’s finished product). In order to ensure that future intent was not driven by a lack of available alternatives (i.e., ‘‘spurious’’ or ‘‘false’’ loyalty), the informant was instructed to comment on an input that was available from at least three vendors. Each informant was mailed a reminder postcard ten days after the initial mailing was sent. It served as a ‘‘thank you’’ note to those who had already complied and a reminder to those who had not completed the survey to date (Dillman, 1978). A total of 209 completed surveys were received. Since 38 surveys were sent back due to incorrect addresses or the inability of the informant to participate (e.g., not thoroughly qualified to evaluate a supplier – buyer relationship) and two surveys arrived after the 4-week final cut-off date, the final effective response rate was 10.7%. This falls within the 10 – 15% response rate that other studies that have utilized the ISM database have registered. 3.2. Measures This section explains our measures and their development process. Standard scale development procedures out-

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lined by Churchill (1979) and Gerbing and Anderson (1988) were followed in order to generate items that tap into the domain of the respective constructs. Since the focal constructs in this study were in large part stimulated by previous theories, an extensive pretest was not required to assess the psychometric properties of the scales a priori. Instead, a small scale pretest (n = 36) was conducted to assess the reliability and evaluate the correlation matrix. Each scale possessed adequate reliability (Cronbach alpha > 0.70), and no unusual pattern was detected in the correlation matrix. In addition, we also met with several procurement professionals in order to receive feedback on the length of the survey, clarity of the instructions, and clarity of the question items. On the basis of feedback secured from the pretest and the informal meetings, several stylistic changes were made to the instructions and questions, and the measurement scales comprising the survey questionnaire were finalized. A description of the multi-item scales appears below. To assess unidimensionality and evaluate convergent and discriminant validity, the measurement model of the latent constructs with these scale items was tested using confirmatory factor analysis. The final scale items utilized for hypotheses testing appear in Appendix A. The completely standardized factor loadings from the CFA measurement model, reliability statistics, means and standard deviations, and the estimated between-construct correlation matrix (phi matrix) are provided in Appendix B. 3.2.1. Order Management Cycle Performance (OPERF) Drawing upon the OMC offered by Shapiro et al. (1992), our newly developed measure taps into a buyer’s perception regarding a vendor’s performance from the time that the order is placed through post-sales assistance. A seven-item scale was constructed to tap into a supplier’s order cycle time, on-time delivery, order accuracy, emergency order fulfillment, billing accuracy, defect rates, and post-sales assistance. It should be noted that our measure is one in which performance of a given vendor is assessed in relation to what can be provided by alternative sources. This is important for our empirical tests as previous research has suggested that taking a relative approach provides a stronger indication of repeat patronage than an individual evaluation (Dick and Basu, 1994). 3.2.2. Trust (TRUST) As past researchers have recommended that trust ought to be conceptualized as a domain-specific variable and not a global trait (Zand, 1972; Kumar, 1996), we remain consistent with the guidance provided about being particularistic with respect to conceptualizing its antecedents. Although many factors can affect trust, we advance that a firm may have confidence in the reliability as well as integrity of the vendor along the lines of a vendor’s OMC performance. Thus, trust arises from a vendor’s OMC

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performance. A seven-item scale was developed based upon the work of Morgan and Hunt (1994), and one item was subsequently removed for its low item-to-total correlation (i.e., TRUST7; Appendix A). 3.2.3. Transaction Cost Advantage (TCA) A three-item scale emanating from the work of Cannon and Homburg (2001) and Noordewier et al. (1990) was developed to assess transaction cost savings. Given that many firms today are looking to minimize procurement costs, this scale taps into the savings that can be realized by buying firms from expenses related to acquisition and possession costs. 3.2.4. Overall Customer Satisfaction (CSAT) Satisfaction is viewed as a ‘‘positive affective state resulting from the appraisal of all aspects of a firm’s working relationship with another firm’’ (Anderson and Narus, 1984, p. 66). This global evaluation of a buying organization’s satisfaction was measured by a three-item scale based on Anderson and Narus (1984, 1990). 3.2.5. Customer Future Intentions (FI) In order to operationalize this forward-looking, behavioral intention, recent writings were consulted in order to develop a construct that captures the essence of a business customer’s desire to continue doing business with a given supplier in relation to others in the evoked consideration set (Anderson and Weitz, 1989; Cannon and Homburg, 2001; Heide and John, 1990; Noordewier et al., 1990). Given that multiple suppliers are available and relied upon for the majority of items that a firm procures, it is necessary to tap into the extent to which the buyer states that it intends to continue giving the supplier a representative share of its future business. 3.2.6. Measurement model evaluation The above multiple-item scales were subjected to a CFA measurement model in order to assess convergent and discriminant validity. The fit was acceptable (v 2 = 483.50; df = 265; GFI = 0.83; AGFI = 0.80; NFI = 0.88; CFI = 0.94; RMSEA= 0.06). Additionally, each latent construct was paired with other constructs and examined for the comparison between a one-factor vs. two-factor solution. All the

Order Management Cycle Performance

.74 (8.67)

.25 (2.62) Trust

H1

H2 H3

latent constructs were statistically distinct; that is, for each pair, a two-factor solution was superior to the one-factor solution. In addition to seeking evidence for discriminant validity, we also sought to assess convergent validity by exploring the variance extracted (VE). The VE for each scale was generally adequate in comparison to Fornell and Larcker’s (1981) threshold of 0.50 or above [Trust (0.74), TCA (0.78), CSAT (0.64), and FI (0.55)]. One exception is OPERF (0.48).

4. Results After examining the five multiple-item scales for unidimensionality, convergent validity, and discriminant validity, the theoretical model was estimated using structural equation modeling in LISREL. The overall the model fit was marginally acceptable (v 2 = 613.09; df = 269; GFI = 0.81; AGFI = 0.76; NFI = 0.85; CFI = 0.91; IFI = 0.91; RMSEA= 0.08). The standardized estimates (t-values in parentheses) are provided in Fig. 2, and the results from the hypotheses tests are presented in the ensuing discussion. The standardized estimates of total and indirect effects can be found in Table 1. H1 predicted that a supplier’s order management cycle performance as seen by the customer is positively related to trust. The LISREL estimate for this relationship was robust at 0.741 and significant at a = 0.05. Therefore, H1 is supported. In H2, it was posited that the customer firm’s trust in the supplier is positively related to the customer firm’s transaction cost advantage. As the LISREL estimate was 0.25 and significant, H2 is also supported. It was advanced that the positive effect of a supplier’s OMC performance on customer firm transaction cost advantage is not only mediated by the trust the customer develops in the supplier (H1 and H2), but also directly and positively related to customer firm transaction cost advantage (H3). The LISREL estimate for H3 was supported at 0.50. Given the indirect (0.19) and direct (0.50) effect of order management cycle performance on transaction cost advantage, it was concluded that the order management cycle performance directly influences the transaction cost advantage more strongly than through trust (see Table 1). This appears plausible. As many

.21 (2.51) H4 Customer Firm Transaction Cost Advantage

Customer Future Intentions

H6

.61 (5.89)

H5 Customer Satisfaction

.50 (4.61)

.70 (7.69)

Fig. 2. Standardized estimates of the model.

N. Bharadwaj, K. Matsuno / Journal of Business Research 59 (2006) 62 – 72 Table 1 LISREL Estimates (Incumbent Model)

Total effects of OPERF

TRUST

TCA CSAT Indirect effects of OPERF

TRUST TCA

On

LISREL estimates (t-value)

TCA FI TRUST CSAT TCA FI CSAT FI CSAT FI

0.69 0.44 0.74 0.48 0.25 0.16 0.18 0.64 0.70 0.61

(8.13) (6.60) (8.67) (6.61) (2.62) (2.50) (2.50) (7.68) (7.69) (5.89)

TCA FI CSAT FI CSAT FI

0.19 0.44 0.48 0.16 0.18 0.43

(2.67) (6.60) (6.61) (2.50) (2.50) (5.08)

Total and indirect effects (standardized).

vendors exist that can provide any given input (Trent and Monczka, 1998), it is logical to advance that OMC performance can have a greater effect on acquisition and possession costs. The theoretical model presented in this article argues that transaction cost advantage positively influences the customer firm’s future intentions both directly (H4) and indirectly (H5 and H6). The LISREL estimates were supportive of the three hypotheses. It is notable that the direct effect of transaction cost advantage on the customer future intentions (0.21) is relatively small in relation to its mediated, indirect effect through customer satisfaction (0.43). This provides support for the importance of

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satisfaction in ultimately increasing the firm’s future purchase intentions. Although not theoretically hypothesized, one reviewer requested that we test an alternative post hoc model (e.g., adding a direct path from TRUST to FI and CSAT, respectively). This appears in Fig. 3. The fit for the alternative model was better than our incumbent model (v 2 = 526.13; df = 267; GFI = 0.82; AGFI = 0.78; NFI = 0.87; CFI = 0.93; IFI = 0.93; RMSEA= 0.07), and represented a statistically significant improvement in our nested model evaluation (Ddf = 2, v 2 = 86.96). Using the post hoc model, we tested all of the a priori hypotheses once again. All the results remain consistent with those using the incumbent model, rendering robust support for our individual hypotheses. Below, we comment further on the post hoc test results, and provide the standardized estimates of indirect effects in Table 2. The alternative model yields several intriguing findings. First, it suggests that trust has a direct and negative impact on customer future intentions ( 0.26; t = 2.18), but a positive indirect effect on the same (0.62), collectively resulting in a total positive effect (0.36). The negative impact of trust on future intentions is unexpected, although the statistical significance of the parameter estimate is marginal. Second, trust seems to have a very strong direct positive effect on customer satisfaction (0.65), but trust by itself has a non-significant indirect effect (0.06; t = 1.81) on satisfaction through transaction cost advantage. Taken altogether, the post hoc model suggests that trust seems to be an important construct strongly emanating from order management cycle performance and has a strong direct, but not mediated, effect on satisfaction. However, as the link between trust and future intentions in the post hoc analysis is enigmatic, caution should be exercised in interpreting the results. Clearly Newly added path -.26 (-2.18)

Order Management Cycle Performance

H1 .76 (8.78)

Trust

H2 .21 (2.00)

Customer Firm Transaction Cost Advantage

H4 .24 (3.09)

H5 .27 (3.96) H3 .49 (4.25)

Customer Future Intentions H6 .81 (5.31) Customer Satisfaction

Newly added path .65 (7.09) Fig. 3. The post hoc model.

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Table 2 Post hoc model Indirect effects of

On

LISREL estimates (t-value)

OPERF

TCA FI CSAT FI CSAT FI

0.16 0.50 0.67 0.62 0.06 0.22

TRUST TCA

(2.04) (7.01) (7.82) (4.52) (1.81) (3.28)

LISREL Estimates (Alternative Model). Indirect Effects (standardized).

additional work should seek to specifically explore whether relational contracting is necessarily inconsistent with future intentions. From a methodological perspective, it is also conceivable that the combination of our imperfect measures with limited discriminant validity and the relatively small sample size made the results unreliable. Specifically, both trust and future intentions are highly correlated with customer satisfaction at 0.80 and 0.76, respectively. This high multi-collinearity alone could have obscured the parameter estimates. Future work should, therefore, seek to replicate the relationship between trust and future intentions presented here with more robust measures, and explore the conditions under which it may vary.

5. Discussion and implications This manuscript has implications for both academics and practitioners. As far as the former, the theoretical contributions are three-fold. For one, this research is to the best of our knowledge the first to operationalize the order management cycle construct and formally test it in empirical research. This is an important contribution as the activities from the time that an order is placed through post-sales assistance offer firms a fertile opportunity to secure a competitive advantage (Shapiro, 1997). Second, this article adds to our understanding of trust. Although trust has been cited as an important social consideration (Morgan and Hunt, 1994), some authors contend that relatively little research has examined the role of trust in supplier selection decisions (Doney and Cannon, 1997). This manuscript reasserts that trust must be studied as a domain-specific variable as a firm may have confidence in the reliability and integrity of the vendor along the lines of OMC performance, but may not trust the vendor in other areas (e.g., dedicating specific assets towards new product development). In addition to identifying a previously neglected antecedent of trust (i.e., trust emanates from a vendor’s demonstrated capability and credibility along the order management cycle), this effort offers that trust in a vendor’s OMC performance has a direct, tangible outcome sought by buying firms: customer firm transaction cost savings. Lastly, this

manuscript empirically investigates a normative prescription of transaction cost theory: to examine factors that can give rise to costs caused by imperfect coordination in buyer – supplier relationships (i.e., transaction costs). Although transaction cost theory claims that higher levels of behavioral uncertainty increase the costs of monitoring and evaluating the performance of exchange partners, that proposition has not been formally tested. Our research suggests that effective OMC performance can lead to trust as the buying firm believes that the given exchange partner is reliable and is not likely to act opportunistically in its order fulfillment, billing, and post-sales activities, thereby reducing the need to closely scrutinize that facet of the exchange relationship. As transaction costs can exceed actual invoice costs in a buyer – supplier relationship (Noordewier et al., 1990) and problems associated with the OMC are much more likely to force customer firms to switch vendors than are productrelated issues (Shapiro, 1997), this research also has important implications for business practice. In particular, customer firms can develop objective operational metrics coinciding with the OMC to assess supplier performance as the information gathered can assist buying firms in identifying areas of efficiency and effectiveness improvement in each supplier interface (Day, 1994; Smeltzer and Manship, 2003). This research suggests that a customer firm engaged in an exchange relationship with a supplier that performs well along the OMC is likely to improve the efficiency of the procurement process. Dell, for instance, serves as an exemplar as its business model mandates attending to such items as a supplier’s order cycle time, accuracy in filling orders, accuracy in billing processes, ontime delivery performance, ability to fill emergency orders, and condition of products on arrival (Treacy and Wiersma, 1993). Thus, Dell has been able to lower acquisition costs by requiring its suppliers to become intimately familiar with its requirements and tolerances, and possession costs by replacing inventory with information. It is, therefore, advisable that firms develop tangible operational metrics, and then critically evaluate their suppliers along those metrics, and remain engaged with high performers that can deliver such instrumental benefits as transaction cost advantage. Although the results are theoretically and practically meaningful, we conclude by offering the limitations of this study and as well as directions for future research. For one, the data secured are cross-sectional in nature. Future research can undertake a longitudinal approach to better understand the relationship developmental processes (Narayandas and Rangan, 2004) that can lead to a reduction in transaction costs. Second, this study relies upon one individual within the organization to serve as the key informant. As some researchers have raised issue with the ability of one individual to comment upon global, organizational-level phenomenon (Kumar et al., 1993), future studies ought to secure perceptions from

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multiple informants within the customer firm, and ideally, be verified with objective measures. Third, it is necessary to more fully examine the concept of customer firm costs. One possibility is to identify and integrate the totality of acquisition, possession and direct product costs involved with using and subsequently disposing of a good or service from a vendor (Ellram, 1995). Lastly, the measures that were used in the empirical tests were highly correlated. Specifically, given the limited evidence of discriminant validity between order management cycle performance and satisfaction in this study and task-related performance and satisfaction in previous inter-organizational research (e.g., Kumar et al., 1992), researchers may wish to critically consider using one or the other in future research. As the order management cycle provides

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a viable basis to secure a competitive advantage, we encourage researchers to build upon the operationalization of order management cycle performance offered here and to further explore the nomological network in which the construct is embedded.

Acknowledgements The authors wish to thank Barry J. Babin, Joseph P. Cannon, Robert Dahlstrom, Robert A. Peterson, and two anonymous reviewers for helpful suggestions on earlier versions of this manuscript, and Rick Boyle and Paul Novak at the Institute for Supply Management (ISM) for assistance with our data collection activity.

Appendix A Scale Items Construct

Item #

Item

Scale format

Source

Order Management

OPERF1

5-point scale (poor to excellent)

Cycle Performance (OPERF)

OPERF2 OPERF3

This supplier’s order cycle time (i.e., lead time between ordering and delivery). This supplier’s on-time delivery performance. This supplier’s ability to fill emergency orders.

OPERF4

This supplier’s accuracy in filling our orders.

OPERF5

This supplier’s accuracy in billing and credit.

OPERF6

The condition of this supplier’s products on arrival (i.e., defect rates). This supplier’s post-sales assistance and support (i.e., complaint resolution, installation, training). With regard to the formal policies and procedures and sales support, we have been very satisfied with this supplier. Our company’s overall working relationship with this supplier has been an unhappy one. Our company is satisfied with how the relationship with this supplier has been. It is highly probable that we will be doing business with this supplier a year from now. Renewal of the relationship with this supplier is virtually automatic. We expect this supplier relationship to last a long time.

Developed from Shapiro et al. (1992) Cannon and Perreault (1999) Developed from Shapiro et al. (1992) Developed from Shapiro et al. (1992) Developed from Shapiro et al. (1992) Developed from Shapiro et al. (1992) Developed from Shapiro et al. (1992) Anderson and Narus (1984, 1990)

OPERF7 Customer Satisfaction (CSAT)

CSAT1

CSAT2 CSAT3 Customer Firm Future Intentions (FI)

FI1 FI2 FI3 FI4 FI5

FI6 Transaction Cost Advantage (TCA)

TCA1

The likelihood of terminating this relationship in the next two years is pretty low. We are likely to increase the proportion of business that we give to this supplier at the expense of the other suppliers that can provide this item. We would strongly consider being involved in a singlesourcing arrangement with this supplier. Relative to other suppliers that can provide this item, this supplier provides us with lower acquisition costs (e.g., order processing costs, delivery monitoring costs, follow-up costs in case of delivery inaccuracy and delay, contract negotiation costs, etc.).

5-point scale (strongly disagree to strongly agree)

5-point scale (strongly disagree to strongly agree)

Anderson and Narus (1984, 1990) Anderson and Narus (1984, 1990) Noordewier et al. (1990) Noordewier et al. (1990) Noordewier et al. (1990), Heide and John (1990). Anderson and Weitz (1989) Cannon and Homburg (2001)

Anderson and Weitz (1989) 5-point scale (strongly disagree to strongly agree)

Noordewier et al. (1990)

(continued on next page)

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Appendix A (continued) Construct

Item #

Item

TCA2

Relative to the other suppliers that can provide this item, this supplier provides us with a greater advantage in terms of possession costs (e.g., lower inventory and warehousing costs, insurance costs, etc.). Relative to other suppliers that can provide this item, this supplier provides us with a greater overall transaction cost saving. In our relationship, this supplier cannot be trusted at times (reverse item). This supplier is perfectly honest and truthful. This supplier can be trusted completely. This supplier can be counted on to do what is right. In our relationship, this supplier is always faithful. This supplier has high integrity. This supplier is someone that we have great confidence in (subsequently removed).

TCA3

Trust (TRUST)

TRUST1 TRUST2 TRUST3 TRUST4 TRUST5 TRUST6 TRUST7

Scale format

Source Noordewier et al. (1990)

Noordewier et al. (1990)

5-point scale (strongly disagree to strongly agree)

Morgan and Hunt (1994) Morgan Morgan Morgan Morgan Morgan Morgan

and and and and and and

Hunt Hunt Hunt Hunt Hunt Hunt

(1994) (1994) (1994) (1994) (1994) (1994)

Appendix B Scale Validation Statistics and Estimated Correlation (Phi) Matrix Construct

Item #

Completely standardized loading

Standard error

t-value

Cronbach alpha

Mean

Standard deviation

Order Management Cycle Performance (OPERF)

OPERF1 OPERF2 OPERF3 OPERF4 OPERF5 OPERF6 OPERF7 CSAT1 CSAT2 CSAT3 FI1 FI2 FI3 FI4 FI5 FI6 TCA1 TCA2 TCA3 TRUST1 TRUST2 TRUST3 TRUST4 TRUST5 TRUST6

0.71 0.81 0.70 0.67 0.71 0.60 0.65 0.83 0.67 0.89 0.81 0.70 0.94 0.85 0.61 0.40 0.87 0.85 0.92 0.66 0.89 0.90 0.91 0.87 0.91

0.05 0.06 0.06 0.06 0.05 0.06 0.06 0.05 0.07 0.05 0.05 0.07 0.05 0.05 0.07 0.09 0.06 0.06 0.05 0.07 0.06 0.06 0.05 0.05 0.05

11.29 13.48 10.96 10.43 11.33 9.01 9.99 14.28 10.44 15.71 13.85 11.29 17.69 15.00 9.46 5.77 15.42 14.67 16.73 10.50 16.15 16.59 16.97 15.60 16.87

0.86

3.65 3.84 3.79 4.00 3.85 4.15 3.91 3.79 2.00 3.78 4.35 3.74 4.06 4.00 3.20 2.87 3.69 3.59 3.57 2.04 3.57 3.44 3.73 3.71 3.77

0.84 0.94 0.96 0.83 0.88 0.89 0.90 0.91 1.09 0.88 0.84 1.07 0.88 0.94 1.02 1.22 1.00 0.96 0.96 1.07 1.01 1.08 0.94 0.98 0.96

Customer Satisfaction (CSAT)

Customer Firm Future Intentions (FI)

Transaction Cost Advantage (TCA)

Trust (TRUST)

0.82

0.85

0.91

0.94

Measurement Model Fit Statistics. v 2 = 483.50; df = 265; GFI = 0.83; AGFI = 0.80; NFI = 0.88; CFI = 0.94; RMSEA= 0.06. Estimated Correlation Matrix (Phi Matrix) OPERF OPERF CSAT

1.00 0.87 (0.03) 29.47

CSAT 1.00

FI

TCA

TRUST

N. Bharadwaj, K. Matsuno / Journal of Business Research 59 (2006) 62 – 72

71

Appendix B (continued)

FI

TCA

TRUST

OPERF

CSAT

0.58 (0.05) 10.78 0.64 (0.05) 12.74 0.74 (0.04) 19.35

0.76 (0.04) 19.62 0.64 (0.05) 12.65 0.80 (0.03) 24.23

FI

TCA

TRUST

1.00

0.61 (0.05) 12.36 0.53 (0.05) 9.76

1.00

0.58 (0.05) 11.25

1.00

Standardized estimate (standard error). t-value.

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