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Impact of boundary-spanning information technology and position in chain on firm performance.


Firms have increasingly used boundary-spanning information technologies (BSIT) to support transactions with their trading partners (Stank, Crum and Arango 1999). BSIT refer to the information technologies (IT), such as electronic data interchange (EDI) and Internet-enabled electronic markets, that facilitate transactions and other partnering activities between firms in a supply chain. According to the U.S. Bureau of Economic Analysis, electronic market transactions reached US$2.94 trillion, or 13.88 percent of total transactions in 2006, as measured by the value of sales in manufacturing, wholesale and retail industries. Although extensive literature has documented that the use of BSIT results in improved performance, such as reduced shipment errors, higher inventory turnover and reduced stockouts (e.g., Srinivasan, Kekre and Mukhopadhyay 1994; Mukhopadhyay, Kekre and Kalathur 1995; Zhu and Kraemer 2002), little research has studied whether firms in different positions in the supply chain (e.g., manufacturers versus distributors) perceive these performance benefits equally or differently. The objective of this paper is to empirically study this research issue using data collected from the U.S. food industry.

It has been argued that intermediaries, such as distributors and retailers, are vulnerable to being displaced by direct sales channels. (1) This phenomenon, that is the displacement of intermediaries from supply chains, has been termed "disintermediation." El Sawy, Malhotra, Gosain and Young (1999, p. 308) have stated that the "conventional structures and strategies in the distribution industry are being torn apart by new IT-intensive business models ... mak[ing] disintermediation of existing channels a serious threat." Others have noted that intermediaries add significant costs to supply chains and that manufacturers, by investing in BSIT, can save much of these costs (Sarkar, Butler and Steinfield 1995; Giaglis, Klein and O'Keefe 2002; Rabinovich and Evers 2003). The Internet is an especially useful technology to lower costs through disintermediation, in that it allows end consumers to transact directly with manufacturers, thus bypassing traditional distributors (Bakos 2001).

Traditional intermediaries not only risk extinction due to direct manufacturer-consumer transactions, but they also face competitive threats from the rise of new types of technology-savvy intermediaries or "cybermediaries" (Sarkar et al. 1995; Giaglis et al. 2002). Using the automobile industry as an example, Bakos (1998) shows how Internet marketplaces facilitate the creation of cybermediaries that can aggregate services previously offered by traditional intermediaries. Internet-based firms, such as Auto-by-Tel, allow consumers to obtain market, price and distribution information on a large variety of automobiles, thus consolidating the (formerly) cumbersome process of searching for new or used vehicles. The rise of the cybermediary has changed the focus of the debate from intermediation versus disintermediation to include the possibility of reintermediation with technology-savvy firms displacing traditional distributors as intermediaries (Chircu and Kauffman 1999; Palvia and Vemuri 1999; Bakos 2001; Rabinovich 2004).

The advent of the Internet, the rise of cybermediaries, and the reintermediation of supply chains, all leave in question the future of traditional intermediaries. In outlining the resource-based view (RBV) of the firm, Barney (1991) stated that to build sustained competitive advantage, a firm's resources must be valuable, rare, imperfectly imitable and have no strategically equivalent substitutes. To the extent that traditional intermediaries can develop and maintain these types of resources, they should be able to achieve sustained competitive advantage. It is clear from the literature that IT resources can help intermediaries in this regard, although there is debate about whether IT resources, alone, are sufficient (Mata, Fuerst and Barney 1995; Bharadwaj 2000; Santhanam and Hartono 2003; Wade and Hulland 2004). It may be that the traditional resources of intermediaries, combined with investments in BSIT, together can lead to viable, long-term strategies.

In this paper, we employ RBV and extended RBV theory, a well-adopted framework in the logistics research literature (e.g., Zsidisin, Ellram and Ogden 2003; Autry, Griffis, Goldsby and Bobbitt 2005; Stank, Davis and Fugate 2005; Hunt and Davis 2008), to develop hypotheses assessing: (1) how the use of BSIT is perceived to impact performance improvement, and (2) the perceived value of BSIT to supply chain intermediaries (distributors and retailers) compared with the value of BSIT to manufacturers. We then use the results from a survey of food industry manufacturers, distributors and retailers, to analyze our hypotheses. We believe that our results shed light on how intermediaries have adapted to the new economy, and whether they are likely to survive into the future. Our key findings are that the use of BSIT does lead to performance benefits in terms of order processing cost reductions with suppliers and customers, inventory reduction and customer satisfaction improvement, and that intermediaries do perceive greater benefits from the use of BSIT than do the manufacturers. Thus, our study makes a significant contribution to the literature by providing additional empirical evidence that the use of BSIT is beneficial, and a unique contribution by comparing the benefits perceived by firms in different positions in the supply chain. To the best of our knowledge, little research has examined the issue from the different perspectives of supply chain members. On the other hand, our data also show that intermediaries have not invested in BSIT to any greater extent than have manufacturers, despite their perception of greater benefits from this technology. Therefore, our study also indicates that there may be potential benefits from BSIT that are not being captured by intermediaries in the food industry.

The rest of the paper is organized as follows. In the next section, we discuss the theoretical basis for this research and develop a number of hypotheses based on the theory. In the following section, we discuss the data and methodology used for our empirical tests. Next, we present the results from our analysis. Finally, we draw conclusions from our research, discuss the limitations and present possible avenues for future research.


There has been much discussion in the RBV literature about the type of resources that can produce sustained competitive advantage (e.g., Barney 1991; Peteraf 1993; Mathews 2002). A firm is said to have a sustained competitive advantage when it is implementing a unique strategy for which other firms face significant impediments in copying (Mata et al. 1995; Mathews 1996; Hunt and Davis 2008). The resources able to produce sustained competitive advantage must be economically valuable, relatively scarce, socially complex and costly to imitate (Barney 1991; Mata et al. 1995). It has been shown that an organization's skills in leveraging IT are a likely source of these resources as they tend to be heterogeneously distributed across firms, reflecting the unique history of a firm (Zhu and Kraemer 2002, 2005). The importance of IT for achieving competitive advantage is echoed by Coates and McDermott (2002) who found that complex skill sets acquired through learning, including technological skills, form the basis of a firm's competencies. Further support is provided by Santhanam and Hartono (2003), who found in their empirical results "strong evidence" that IT capabilities have a positive and sustained impact on firm performance, consistent with RBV theory. Finally, through interviews, Edwards, Peters and Sharman (2001) indicated that leading companies are able to exploit IT to redesign their business processes around the needs of their customers, whereas other firms without this technological expertise fall behind their competitors.

While the RBV has mainly focused on the internal aspects of a firm, it has also been extended to analyze the relationships between firms. Dyer and Singh (1996) identified four determinants of interorganizational competitive advantage: relationship-specific assets, knowledge-sharing routines, complementary resources and capabilities, and effective governance, all of which offer important theoretical implications for the effects of BSIT use on supply chain performance. First, when supply chain partners invest in relation-specific assets, they may gain relational rents. Although BSIT has become increasingly standardized (Carr 2003), "the process of integrating the components to develop a coherent infrastructure tailored to a firm's strategic context is complex and imperfectly understood" (Zhu and Kraemer 2002). In that sense, firms can create unique, relationship-specific processes enabled by BSIT, especially for "back-end" integration processes, such as procurement (Dyer 1996). Second, BSIT may improve the absorptive capacity of supply chain partners through which firms are able to recognize and assimilate information and knowledge within their partnership (Malhotra et al. 2005). Research has shown that information sharing or knowledge sharing between supply chain partners can improve their performance (Subramani 2004; Dyer and Hatch 2006). Supply chain partners can increase partner-specific absorptive capacity by designing interfirm routines (Dyer and Singh 1996), which can effectively be facilitated through BSIT. Third, relational rent can also be created through complementarity among resources within a supply chain. As outlined by Zhu and Kraemer (2002), "complementarity represents an enhancement of resource value and arises when a resource produces greater returns in the presence of another resource than it does alone. "The authors argued that IT infrastructure and BSIT such as electronic markets are complementary resources. They found, through empirical analysis, that this complementarity increases inventory turns more than do the two resources alone. Fourth, governance plays a key role in the creation of relationship rents because it influences transaction costs (Dyer and Singh 1996). BSIT use may facilitate effective governance between supply chain members, thereby leading to improved supply chain performance. Supply chain partners are subject to bounded rationality and opportunism (Crover and Malhotra 2003; Williamson 2008). Bounded rationality arises from the limited ability to receive, store, retrieve and communicate information without error, whereas opportunism results from behaviors, such as cheating, lying and subtle forms of violation of agreements due to information asymmetry (Grover and Malhotra 2003; Williamson 2008). BSIT use expands a firm's information processing capability (Clemons and Reddi 1993) and provides enhanced supply chain visibility and mitigated information asymmetry (Zhu 2004), both of which reduce the chances of bounded rationality and opportunism.

In addition to the papers cited above, there is an extensive literature on the contribution of BSIT to performance improvement. A number of these papers looked specifically at the advantages of EDI over more traditional forms (e.g., telephone calls or faxes) of confirming orders, exchanging documents such as purchase orders and acknowledging payments. For example, in a study of the continuous replenishment program (CRP) used by the Campbell Soup Company with a number of its retail customers, Lee, Clark and Tarn (1999) found that EDI (an integral part of CRP) contributed to lower inventories and reduced stockouts. These benefits accrued to both Campbell and its retail customers. Ahmad and Schroeder (2001) showed that the extent of EDI use significantly improved delivery performance, given the superior quality of information exchanges between supply chain firms. In a field study of the logistics operations of Chrysler assembly centers, Srinivasan et al. (1994) found that EDI facilitated just-in-time (JIT) shipments, leading to significantly lower levels of shipment errors. A further paper (Mukhopadhyay et al. 1995) concluded that EDI use contributed to a cost savings for Chrysler of US$100 per vehicle.

A number of studies have extended BSIT analysis to include the impact of Internet-based electronic commerce on firm performance. In this regards, Zhu and Kraemer (2002) found that electronic commerce technologies are positively associated with inventory turns and negatively associated with cost of goods sold. The authors concluded that firms that use electronic commerce technologies are more agile and capable of competing in dynamic markets than are firms without these technologies. In a study of the aircraft parts industry, Choudhury, Hartzel and Konsynski (1998) showed that although inventory levels were unaffected by the use of electronic commerce, there were improvements in identifying parts and reducing aircraft downtime that could be attributed to these technologies. From a survey of U.K. manufacturers and service providers, Frohlich and Westbrook (2002) found that Web-based demand integration led to improved performance for the manufacturers but not necessarily for the service providers. Finally, from a survey of 416 customers of a major Internet retailer, Boyer and Olson (2002) concluded that companies realize performance benefits from utilizing the Internet as a supply management tool.

In summary, the literature on both EDI and Internet-based electronic commerce technologies supports the view that BSIT use can result in improved performance. As detailed in the next section of this paper, we measure performance improvement in terms of perceived order processing cost reduction, perceived inventory level reduction and improved customer satisfaction. Therefore, we propose the following three hypotheses:

H1: The use of BSIT is positively associated with perceived order processing cost reduction.

H2: The use of BSIT is positively associated with lower perceived inventory levels.

H3: The use of BSIT is positively associated with higher perceived customer satisfaction.

A number of writers have noted that intermediaries that invest in IT are less likely to be disintermediated, and are more likely to achieve sustained competitive advantage. Bailey and Bakos (1997), in their exploratory study of electronic intermediaries, found that while some traditional intermediaries may become less important in the new economy, those that can undertake new functions, such as providing relationships characterized by trust and ensuring the integrity of markets, may assume added significance within the supply chain. Dresner, Yao and Palmer (2001) stated that the best defense for distributors against distintermediation may be to invest in IT. According to the authors, it is through the implementation of IT systems that distributors can best ensure competitive success. Bakos (1997) stated that the provision of electronic intermediation services may produce potentially large rewards. Giaglis et al. (2002) noted that electronic markets provide opportunities for intermediaries, which can combine traditional distribution functions, such as warehousing and transportation, with the provision of key data, such as tracking and delivery information, to other supply chain members. Bakos (1998) discussed the growth of the "one-stop" intermediary that can use IT to integrate information previously provided by a variety of firms.

Chircu and Kauffman (1999) stated that traditional intermediaries will "fight back" against disintermediation by seeking to strengthen links with other firms in their supply chain. Narayandas, Caravella and Deighton (2002) noted that there is less likely to be disintermediation when distributors perform such boundary-spanning roles as customer identification for manufacturers. Malone, Yates and Benjamin (1987) described how computer-based markets in the pharmaceutical industry have allowed distributors to establish electronic links within the industry in an attempt to monopolize business. Finally, Sherer and Adams (2001) concluded from their exploratory research that intermediaries are particularly important in identifying market opportunities for other supply chain members, and that IT plays a key role in helping intermediaries in this regard.

In short, it is clear that intermediaries that use BSIT may perform better than those that do not, and be less likely to be disintermediated. It can be argued further, based on the above discussion, that intermediaries will perceive greater performance benefits from the use of BSIT than will manufacturers, given the threat of disintermediation. Therefore, we propose the following three hypotheses:

H4: Intermediaries (distributors and retailers) perceive greater order processing cost reduction from the use of BSIT than do manufacturers.

H5: Intermediaries (distributors and retailers) perceive greater inventory reduction from the use of BSIT than do manufacturers.

H6: Intermediaries (distributors and retailers) perceive greater customer satisfaction improvement from the use of BSIT than do manufacturers.


Operationalization of Constructs

Based on the hypotheses developed above, Figure 1 illustrates our proposed research model. The dependent variables for the model measure aspects of a firm's perceived performance benefits from the implementation of BSIT. Chwelos, Benbasat and Dexter (2001) outlined several benefits that may be associated with the use of BSIT, including reduced communication and administration cost, and the enabling of supply chain initiatives, such as JIT inventory management. Min and Galle (1999) specified transaction cost and inventory cost reductions as potential benefits arising from the use of BSIT. Order processing cost and inventory cost reductions have also been found in a number of empirical studies on the use of BSIT systems (e.g., Mukhopadhyay et al. 1995; Mackay and Rosier 1996; Crum, Johnson and Allen 1998; Stank et al. 1999).


In order to measure perceived performance benefits, data were collected on a firm's perceptions of (1) reduction in order processing costs with its suppliers, (2) reduction in processing costs of customer orders and (3) inventory reductions due to the implementation of BSIT initiatives. As well, in order to capture a broader assessment of BSIT initiatives, data were collected on (4) perceived customer satisfaction improvement with these initiatives.

The hypothesized independent variable in the model is the use of BSIT by a firm. We further decompose BSIT into BSIT use with suppliers and BSIT use with customers. In order to assess the use of BSIT, managers were asked a number of questions related to the use of BSIT to conduct business with both suppliers and customers. In particular, managers were asked whether their firms conducted the following activities over the Web with either suppliers or customers: shared product information, shared pricing information, shared order status information and shared order tracking information. Separate boundary-spanning measures were constructed for cooperative arrangements with suppliers and customers by aggregating across the activities described above.

Position in Chain is assessed by dummy variables indicating whether the manager responding to our questionnaire worked for a manufacturer/processor, distributor/wholesaler or retailer. These dummy variables are employed to determine the association of a firm's position in the supply chain with perceived performance benefits from BSIT use.

A control variable for firm size (revenue of firm) is added to account for possible firm size effects on perceived performance benefits (Walton 1994; Daugherty, Germain and Droge 1995; Premkumar, Ramamurthy and Crum 1997; McGowan and Madey 1998). As noted by Wade and Hulland (2004) and others, IT resources may only be one factor in the achievement of performance benefits.

In summary, the model is estimated with four different dependent variables measuring perceived performance benefits. Two variables measure the use of BSIT--one for the cooperative use of IT with suppliers and the other for the cooperative use of IT with customers. Dummy variables are used to assess a firm's position in the supply chain. Finally, a control variable is used to measure firm size.

Data and Sample

Responses from a survey in the food industry provide the data for this study. We use the food industry for a number of reasons. First, the food industry composes a large part of the U.S. industrial base, employing about 14 percent of the country's workers and accounting for 9 percent of gross domestic product. Retail sales from food stores, restaurants and bars are over US$954 billion annually (U.S. Census 2006). Second, the food industry has been described as a laggard in the use of IT (Whang et al. 2000), although a more recent survey of food industry managers has found substantial progress in the use of e-commerce since 2000 (Food Logistics 2003). Information exchange, collaborative forecasting, the use of electronic payment systems and other e-commerce initiatives can help overcome some of the inefficiencies that prevail in the industry. According to one consulting firm, out-of-stock products cost the food industry US$7-12 billion per year, and the industry could save US$30 billion annually by reducing excessive inventory levels (McPoland 2004). Inventory control is especially important at the retail level where margins are typically only 1-2 percent of sales and supermarket chains and independent grocers face increasing threats from competition by super-centers and warehouse club stores (Kinsey 2000; Ritter 2004). Third, food intermediaries, including distributors and retailers, are especially vulnerable to industry trends towards the adoption of e-commerce technologies. Whang et al. (2000) stated that distributors represent a "significant bottleneck" to information flows in the food industry. Many of the distributors use proprietary software for handling orders and executing payments and make it difficult for manufacturers and retailers to share information. "Improvements in IT and the establishment of standards in the industry are often viewed as a threat that could make distributors obsolete or relegate the industry to a purely commoditized business" (Whang et al. 2000, p. 3). Trends toward consolidation in the retail and processor sectors of the industry also increase the threat to distributors. Over the last several years, supermarket chains have been growing larger through mergers and gaining market share from their independent rivals, resulting in the top five food retailers accounting for about one-third of U.S. sales (Holz-Clause and Geisler 2007). Large retailers often bypass distributors in the supply chain, buying directly from manufacturers and operating their own distribution centers. As well, retailers may be driven from business unless they are to respond to heightened competition, possibly by increasing efficiency through investments in BSIT.

The development of the survey and the sampling procedures followed the guidelines suggested by Dillman (1978). The survey instrument was developed based on variables used in previous studies (Palmer and Markus 2000) and from a series of interviews conducted with 15 executives in the food industry. Multiple items are used to measure the research variables. Most items are measured using a five-point Likert scale with 1 indicating "strongly disagree" and 5 indicating "strongly agree," while categorical items are used to determine firm size and position in chain. The extent to which boundary spanning resources are deployed is measured by the scale, no use (0), light use (1), moderate use (2), and heavy use (3) (see the Appendix). The survey was tested with 15 executives in the food industry, as well as with a number of colleagues who are versed in survey research.

The survey sample was derived from the database of subscribers to Food Logistics magazine. Food Logistics provided the researchers with subscriber names, firm addresses, firm position in chain, phone numbers and job titles. The survey instrument was targeted at managers in firms across the food industry supply chain, with respondents representing managerial positions at these firms. A stratified random sampling procedure was used to ensure that three levels of the supply chain (i.e., manufacturers, distributors and retailers) were sampled.

A total of 2,126 surveys were sent to firms at the three levels of the supply chain: 727 to manufacturers or food processors, 704 to distributors or wholesalers and 695 to retailers and food service operators. Two rounds of mailings were conducted with an interval of 3 weeks, with one round of reminder cards sent in between. Fifty-eight surveys were returned with delivery problems, reducing the effective sample size to 2,068. A total of 238 responses were received from the manufacturers, distributors and retailers for a response rate of 11.51 percent. Among the returned surveys, 4 were discarded since fewer than 80 percent of questions were answered, leaving 234 usable surveys. Although the response rate is low, it is within a range of published surveys of managers (e.g., 12.3 percent in Sanders and Premus 2005; 10.20 percent in Wisner 2003; 11.5 percent in Zhao, Droge and Stank 2001; 9 percent in Sinkovics and Roath 2004).

To address the potential for nonresponse bias, answers to a number of key questions (i.e., related to IT resources and firm size) from late respondents were compared with answers from earlier respondents to determine if there were statistical differences (Armstrong and Overton 1977; Mentzer and Flint 1997). Prior research has found that the profile of late respondents may resemble those of nonrespondents (Biemer 1991; Malhotra and Grover 1998). By performing a MANOVA test for these key questions, no significant differences were found between the early and late respondents (p>0.75), suggesting that nonresponse bias was not a problem. Finally, tests were conducted for common method bias. As discussed in Podsakoff, Mackenzie, Lee and Podsakoff (2003) and Podsakoff and Dalton (1987), a widely used technique to test for common method bias is to use factor analysis to determine if all of the constructs load on a single factor. None of the correlation coefficients between the constructs were within two standard deviations of 1.0 (which would indicate perfect, positive correlation) or - 1.0 (which would indicate perfect, negative correlation), thus providing evidence that there was no single factor that could account for all of the variance in our data. Hence, common method bias was thought not to be a problem.

Responses to our survey were received from all segments in the supply chain, with 23.7 percent of the respondents representing manufacturing firms, 41.1 percent representing distribution establishments and 35.2 percent representing retailers and food service operators (chiefly, from the retailer's head office or distribution center. The customers of the retail firms were generally internal customers, i.e., individual stores or outlets). Size of firms ranged from under US$10 million in sales (32.9 percent of respondents) to over US$5 billion (7.4 percent of respondents), reflecting the general makeup of the food industry. Table I presents a description of the respondents and Table II presents the descriptive statistics for all variables.

Descriptive Statistics for the Respondents

                             Number of Respondents  % of Respondents

Position in chain
  Manufacturer                        56                   23.7
  Wholesaler/Distributor              97                   41.1
  Retailer/Food                       83                   35.2
  Service Operator

Annual sales
  <$1 Million                         25                   11.4
  $1-5 million                        26                   11.8
  $6-10 million                       29                   13.2
  $11-50 million                      46                   20.9
  $51-100 million                     22                   10.0
  $101 million-S 1 billion            45                   20.5
  $1-5 billion                        13                    5.9
  $5-20 billion                       10                    4.5
  <$20 billion                         4                    1.8

Note: All dollars are In US currency.


Descriptive Statistics and Correlation Matrix (N=215)

                 Mean   SD    Minimum  Maximum      1            2

1. Order         3.67  0.84      1        5       1
processing cost
reduction with

2. Order         3.54  0.93      1        5       0.67 ***    1
processing cost
reduction with

3. Inventory     3.09  0.89      1        5       0.51 ***    0.40 ***
reduction (IR)

4. Customer      3.36  0.85      1        5        0.49 ***   0.63 ***

5. BSIT with     0.98  0.91      0        3        0.30 ***   0.28 ***

6. BSIT with     0.70  0.83      0        3        0.24 ***   0.35 ***

7. Distributor   0.38  0.49      0        1        0.11       0.15 *

8. Retailer      0.37  0.48      0        1        0.09      -0.07

9. Firm Size     4.23  2.09      0        9        0.31       0.32

              Mean   SD    3        4         5      6     7        8

1. Order      3.67  0.84

2. Order      3.54  0.93

3. Inventory  3.09  0.89  1

4. Customer   3.36  0.85  0.50***    1

5. BSIT with  0.98  0.91  0.18**    0 29     1
suppliers                           ***

6. BSIT with  0.70  0.83  0.19**    0.33     0.63   1
customers                           ***      ***

7.            0.38  0.49  0.07      0.10    -0.01   0.01   1

8. Retailer   0.37  0.48  0.08     -0.02     0.06   0.02  -0.61    1
(RT)                                                       ***

9. Firm Size  4.23  2.09  0.03      0.15 *   0.42   0.40   0.14   -0.02
(SIZE)                                       ***    ***    *

*p <0.05; ** p<0.01; *** p < 0.001.


We use multiple regressions to estimate our models, with the results presented in Table III. The first column indicates the results for the estimation of order processing cost reduction with suppliers; the second column the results for the estimation of order processing cost reduction with customers; the third column the results for the estimation of inventory reduction; and the fourth column the results for the estimation of customer satisfaction. In the first column, the coefficient for BSIT with suppliers is positive and significant (p < 0.05). In the second column, the coefficient for BSIT with customers is also positive and significant (p<0.01). These results provide support for H1, that the use of BSIT reduces perceived order processing costs with suppliers and customers. In the third column, the coefficient for BSIT with customers is positive and marginally significant (p < 0.10) while the coefficient for BSIT with suppliers is insignificant, lending partial support to H2 that the use of BSIT with customers results in perceived inventory reduction. In the fourth column, the coefficient for BSIT with suppliers is positive and marginally significant (p < 0.10), while the coefficient for BSIT with customers is positive and significant (P <0.05), lending support to H3 that the use of BSIT with both suppliers and customers results in perceived customer satisfaction improvement (although, as might be expected, the result is stronger for BSIT with customers, since it likely has a more direct effect on customer satisfaction).

Estimation of Perceived Benefits (Standard Errors in Parentheses)

                 Order            Order       Inventory      Customer
             Processing Cost    Processing    Reduction    Satisfaction
             Reduction with   Cost Reduction     (IR)          (CS)
               Suppliers      with Customers
                (OPCRS)          (OPCRC)

Intercept       2.86 ***          2.88 ***       2 70 ***    2.98 ***
               (0.14)            (0.16)         (0.16)      (0.15)

BSIT with       0.18 **                          0.12        0.13 +
supplier       (0.06)                           (0.09)      (0.08)

BSIT with                         0.31 ***       0.15.+      0.26 **
customer                         (0.08)         (0.09)      (0.09)

Distributor    0.35 *             0.20           0.35 *      0.25 +
              (0.14)             (0.15)         (0.15)      (0.14)

Retailer       0.37 **           -0.03           0.34 *      0.09
              (0.14)             (0.15)         (0.15)      (0.14)

Firm size      0.09 **            0.09 **       -0.04       -0.01
              (0.03)             (0.03)         (0.03)      (0.03)


N            219                215            217         216

F             10.61 ***          11.17 ***       3.43 **     6.58 ***

Adjusted       0.15               0.16           0.05        0.11

+ p < 0.10; *p < 0.05; **p < 0.01.

Since the omitted dummy variable is for manufacturer, manufacturing firms serve as the base case for comparison purposes. The coefficients for the two dummy variables measuring position in chain (i.e., distributor and retailer) are positive and significant in the estimation of order processing cost reduction with suppliers (p <0.05 and <0.01, respectively), but insignificant in the estimation of order processing cost reduction with customers. These results lend partial support for H4 in that intermediaries perceive greater benefits in terms of order processing cost reduction with suppliers from the use of BSIT than do manufacturers. The coefficients for the distributor and retailer dummy variables are positive and significant in the estimation of inventory reductions (p <0.05), lending support for H5, that intermediaries perceive greater benefits in terms of inventory reductions from the use of BSIT than do manufacturers. The coefficient for the distributor dummy is marginally significant in the customer satisfaction estimation (p <0.10), while the coefficient for the retailer dummy is insignificant. These results lend partial support for H6 that at least some intermediaries (i.e., distributors) perceive greater benefits in terms of customer satisfaction improvement from the use of BSIT than do manufacturers. Overall, these results suggest that intermediaries perceive greater benefits through investments in BSIT than do manufacturers. These findings are consistent with the work of Chircu and Kauffman (1999) suggesting that middlemen perceive that they can invest in technologies and processes that allow them to maintain their position in the supply chain, thus avoiding disintermediation.

Although the intermediaries perceive greater benefits from the use BSIT, further analyses show that the intermediaries do not use BSIT to a greater extent than do the manufacturers, suggesting that the intermediaries' actions in adopting BSIT may lag their perception of the benefits of this technology. This result is illustrated in Table IV. BSIT use with both suppliers and customers is measured for both manufacturers and intermediaries (distributors and retailers) and t-tests are performed to assess mean differences. Neither of the t-tests is significant.

T-Tests for Boundary-Spanning Information Technologies (BSIT) Use
Between Manufacturers, Distributors and Retailers

                              Number of    BSIT with  BSIT with
                             Observations  Suppliers  Customers

Manufacturers                    56          0.94       0.70
Distributors and retailers      180          0.96       0.68
T statistics (HO: diff = 0)                 -0.19       0.17

T-statistics are not significant at p < 0.10.

As noted earlier in the paper, it has been shown that an organization's skills in leveraging IT are an excellent way to achieve competitive advantage (Zhu and Kraemer 2002, 2005). As indicated by Coates and McDermott (2002), the complex skill sets acquired through learning technological skills can form the basis of a firm's competencies. In addition, Santhanam and Hartono (2003) found "strong evidence" that IT capabilities have a positive and sustained impact on firm performance, consistent with RBV theory. Therefore, under-investment in BSIT by intermediaries may result in a weakened competitive position and suboptimal performance. The result that intermediaries in our sample perceive greater benefits from BSIT than do manufacturers, but do not invest in BSIT to a greater extent than do manufacturers, may be an indication of such an under-investment.

The control variable in the model for size is positive and significant in the estimation of order processing cost reduction with suppliers and customers, suggesting that larger firms perceive greater benefits from the use of BSIT than do their smaller competitors. This is a significant result given the consolidations that have occurred in the food industry. The result suggests that small food operators do not recognize the importance of BSIT to the same degree as their larger competitors. To the extent that BSIT can reduce costs and improve performance, the smaller firms may become less cost competitive over the long run if they under-invest in IT.

Finally, the F statistics for the regressions are between 3.43 and 9.01, all significant at the 1 percent level. The adjusted [R.sup.2] values lie between 0.05 and 0.16, indicating a reasonable fit for our models.



The food industry supply chain is an important component to the U.S. economy. Although firms in this industry have lagged other firms in the adoption of e-commerce technologies (Whang et al. 2000), the recent trend toward consolidation in the industry, and the competitive pressure posed by dominant players in the supply chain, have accelerated the adoption of IT-based solutions. IT adoption, combined with industry consolidation, pose a threat to traditional food industry distributors and retailers (and to intermediaries in other industries as well) as they risk being disintermediated from the supply chain, supplanted by other types of business enterprises (e.g., club stores) or replaced by technology-savvy cybermediaries.

Using data collected from the food industry through a mail survey, we estimated a series of multiple regressions, and found the use of BSIT results in improved perceived operational performance, measured by order processing cost reduction, decreased inventory levels and improved customer satisfaction. We also found that intermediaries (distributors and retailers) perceive greater performance improvement from the use of BSIT, compared with manufacturers. These results suggest that the use of BSIT is perceived to be important to a firm's success, and is perceived to be more important for intermediaries than for manufacturers. However, our data also indicate that intermediaries are not investing in BSIT to any greater extent than are manufacturers, despite the greater perceived importance of BSIT to intermediaries. To the extent that distributors and retailers under-invest in BSIT, they may leave themselves vulnerable to becoming less cost-competitive, and ultimately to being disintermediated.

Managerial Implications

Stank et al. (1999) detailed the importance of BSIT to firms operating in the food industry. The authors stated (p. 24):
  Strong information linkages are essential to support
  inter-organizational communications. While these linkages could take
  any number of forms, the focus must be placed on providing access to
  timely, accurate information ... Both EDI and the Internet allow
  businesses to quantify sales, define requirements, and trigger
  production and inventory replenishment ... Rather than relying upon
  sales forecasts, inventory replenishments are driven by precise sales
  information regarding specific stock items in the market.

In their empirical study of food companies, the authors showed that the use of EDI significantly decreased inventory levels, order cycle time and order cycle variance. Coupled with our own results indicating that BSIT are positively related to perceived benefits from order cost reductions with suppliers and customers, as well as from inventory reductions and customer satisfaction, there is a strong case that can be made for the importance of BSIT in the food industry. Further studies have found that BSIT are also important in the achievement of performance success and competitive advantage in other industries (Zhu and Kraemer 2002, 2005; Paulraj and Chen 2007; Huang, Gattiker and Schroeder 2008). The practical implication is that firms need to make investments in BSIT in order to maintain or advance their competitive position.

Maintaining or advancing competitive positions is especially important for distributors and retailers that run the risk of disintermediation. Intermediaries can enjoy better relationships with their customers and suppliers if they can find ways to use BSIT to communicate with these firms. If intermediaries improve supply chain relationships, it stands to reason that other firms in the supply chain will not look for ways to take intermediaries out of the system. So our research supports the idea that BSIT improvements can reduce the risk of disintermediation. (2) Therefore, our main message is that intermediaries (i.e., distributors and retailers) should invest in BSIT in order to achieve performance benefits. These BSIT include online ordering capabilities, real-time shipment tracking, online pricing information, real-time inventory status and real-time order status information. By investing in BSIT, intermediaries may be able to add value to their role in the supply chain, for example, by using BSIT to integrate upstream producers and downstream retailers (e.g., through information sharing, collaborative forecasting, etc.). Certainly, the growth of online intermediaries (e.g., is a good example of how BSIT can be used to the advantage of intermediaries in order to consolidate and advance their positions in supply chains.

Although the advent of electronic commerce has led producers to consider disintermediation as a means of cutting costs and increasing profits, producers have found that realizing these windfalls is not that easy. Developing electronic commerce expertise to communicate directly with retailers is costly, including, for example, the costs associated with developing and maintaining sophisticated Web sites, with managing large-scale database systems (e.g., scanner data from retailers), and with marketing to draw and maintain online customers. By making BSIT investments, distributors can both increase their value in the supply chain and save producers from having to make expensive investments in IT.

Limitations and Future Research

The paper has a number of limitations. Perhaps, most importantly, the analysis is based on the perceptions of managers. In particular, the dependent variables in the models are various types of perceived benefits. To the extent that these perceived benefits do not correlate with actual benefits, then the results of the estimations may be misleading. Future research could use benefit measures from archival data to validate our findings. The cross-sectional analysis is also a limitation of the study in that we cannot clearly conclude that investments in IT today lead to future performance improvements. In order to overcome the limitations of cross-sectional research, it would be helpful if an analysis of the impact of IT investments could take place over time. This approach would require resurveying respondents to determine if investments in IT did actually lead to performance improvements. The analysis is also based on a sample from a single industry with a limited response rate. Although the food industry is one of the largest in the United States in terms of contribution to gross domestic product, the results may not be generalizable to other industries or to other countries. A wider analysis of managers across industry groups and countries would increase the generalizability of the results.


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Survey Questions Used in the Analysis

1. Boundary Spanning IT Use

Please indicate whether you currently conduct or plan to conduct within a year any of the following activities over the Web. Please circle the appropriate number in each row (no use to heavy use):

Obtain Product Information from Suppliers

Obtain Pricing Information from Suppliers

Obtain Order Status from Suppliers

Track Orders from Suppliers

Obtain Product Information from Customers

Obtain Pricing Information from Customers

Obtain Order Status from Customers

Track Orders from Customers

2. Perceived Firm Performance Benefits

We are interested in determining what you perceive as the benefits from electronic commerce.

Please answer each of the following questions by circling the appropriate number, even if you currently do not use electronic commerce (strongly disagree to strongly agree).

Order Processing Cost Reduction with Suppliers

Reduce the cost of placing orders with suppliers

Reduce the paperwork when ordering from suppliers

Reduce labor costs in placing orders from suppliers

Order Processing Cost Reduction with Customers

Reduce the cost of processing customer orders

Reduce paperwork when receiving customer orders

Reduce labor costs in receiving orders from customers

Inventory Reduction

Reduce our inventory levels

Increase inventory turnover

Reduce safety stock

Customer Satisfaction

Allow the provision of better levels of customer service

Increase customer satisfaction

Lead to fewer customer complaints

3. Position in Chain

Which area of the food industry contributes the most revenues to your operations (Circle one option only).

1. Food processing or manufacturing--grocery

2. Food processing or manufacturing--foodservice

3. Supermarket chain

4. Full-line grocery wholesale distribution

5. Foodservice distribution

6. Full-line convenience store wholesale distribution

7. Third-party warehouse/transportation/logistics provider

8. Restaurant/Foodservice Chain

4. Firm Size

Please indicate the total revenues for your company (all locations) in 2001 (or fiscal year 2001) by circling the appropriate number:

1. Less than $1 million

2. $1-5 million

3. $6-10 million

4. $11 million-$50 million

5. $51 million-$100 million

6. $101 million-$1 billion

7. Over $1 billion but less than $5 billion

8. $5 billion to $20 billion

9. $20 billion or above

(1) Distributors typically purchase products from manufacturers and then provide an assortment of these products to their customers, typically retailers. Retailers, on the other hand, sell directly to end users. Since neither distributors nor retailers actually produce products, they are both subject to potential disintermediation, i.e., manufacturers bypassing the intermediaries to sell directly to end users.

(2) We thank an anonymous referee for this point.


Lehigh University


University of Maryland


Principia College

Yuliang Yao (Ph.D., University of Maryland) is an assistant professor in the College of Business and Economics at Lehigh University in Bethlehem, Pennsylvania. His research interests include the business value of information systems in supply chains, electronic commerce, interorganizational information systems, incentive mechanisms in supply chain collaborations, supplier-managed inventory, collaborative planning and forecasting and replenishment. Dr. Yao's articles have appeared in Management Science, Decision Support Systems, Electronic Markets, Supply Chain Management Review, Transportation Journal, Transport Economics and Policy and the International Journal of Electronic Business Research.

Martin Dresner (Ph.D., University of British Columbia) is professor of Logistics, Business and Public Policy at the R.H. Smith School of Business, University of Maryland at College Park, Maryland. He serves as Editor of Research in Transportation Economics, and his activities with professional organizations include a term as president of the Transportation and Public Utilities Group and the Transportation Research Forum. Dr. Dresner has testified before the House Aviation Subcommittee and has worked on consulting projects for a number of organizations, including the Maryland Aviation Administration and the U.S. Department of Energy. His research focuses on logistics management and air transport policy. Dr. Dresner co-authored a book on supply chain management, and he has published papers in leading transportation, logistics and supply chain journals.

Jonathan W. Palmer (Ph.D., Claremont Graduate University) is the president of Principia College in Elsah, Illinois. He has a wide range of research interests within information systems, including the strategic use of information technology, electronic commerce, supply and demand chain management, the development of networked capabilities for competitive advantage and the design of appropriate user interfaces. Dr. Palmer's consulting assignments have included work with firms in retailing, food, mobile commerce, energy, construction, aerospace and the U.S. government. Before assuming the presidency of Principia College, he served on the faculties of the University of Oklahoma, the University of Maryland and at William & Mary College, where he also was academic dean.
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Author:Yao, Yuliang; Dresner, Martin; Palmer, Jonathan W.
Publication:Journal of Supply Chain Management
Article Type:Report
Geographic Code:1USA
Date:Sep 22, 2009
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