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Retailer power and supplier welfare
Pergamon Journal of Retailing 77 (2001) 379 –396
Retailer power and supplier welfare: The case of Wal-Mart Paul N. Blooma,*, Vanessa G. Perryb a
Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC 27599-3490, USA George Washington University, School of Business and Public Management, Washington, DC 20052
b
Received 2 April 1999; accepted 21 April 2001
Abstract Whether retailers have become more powerful than manufacturers in recent years continues to be a burning question in the trade press and academic literature. Our research adds fresh fuel to the fire by looking at whether Wal-Mart, the largest retailer in the United States, has exerted power over its suppliers and squeezed them financially. Previous academic research on retailer power has looked largely at food stores, but we extend this perspective into nonfoods by using Compustat data as a source. Our analysis of these data indicates that the answer may be more complex than a simple yes or no. We find that Wal-Mart suppliers holding a small share of their respective markets do not perform relatively as well financially when they have Wal-Mart as one of their primary customers. However, large-share suppliers to Wal-Mart perform better than their large-share counterparts reporting retailers other than Wal-Mart as their primary customers. This indicates that suppliers who seek Wal-Mart’s wide market reach may derive benefits from using this association if it can be used to strengthen their market positions. Those that fail in this goal, however, may find their profits squeezed and do better by shifting their retail channel focus elsewhere. © 2001 by New York University. All rights reserved.
1. Introduction There has been considerable debate in the trade press and academic literature over whether a significant shift has taken place in the relative power of retailers and manufacturers of consumer products. In general, the trade press has suggested that retailers are increasing their relative power, using it to extract concessions from manufacturers such as merchandising
* Corresponding author. Tel.: ⫹1-919-962-3222; fax: ⫹1-919-962-7186. E-mail address: [email protected] (P.N. Bloom), [email protected] (V.G. Perry). 0022-4359/01/$ – see front matter © 2001 by New York University. All rights reserved. PII: S 0 0 2 2 - 4 3 5 9 ( 0 1 ) 0 0 0 4 8 - 3
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support, trade deals, and slotting allowances (Johnson, 1988; Elman & Hughes, 1988; Buzzell, Quelch & Salmon, 1990; Bowman, 1997). The academic literature, however, has yet to provide conclusive evidence that such a shift has occurred. Several researchers have posed theoretical challenges to the existence of such a shift (Kim & Staelin, 1998; Lariviere & Padmanabhan, 1997; Sullivan, 1997). Moreover, empirical academic work has not shown any significant or consistent improvement in retailer financial performance relative to manufacturers in recent times, which one would expect as a result of a power shift (Farris & Ailawadi, 1992; Messinger & Narasimhan, 1995; Ailawadi, Borin & Farris, 1995). In her review of the work on retailer power, appearing concurrently in this journal, Ailawadi (2001) concludes that: “The conventional wisdom that retailers have grown more powerful relative to packaged goods manufacturers in the packaged goods industry has not been supported by empirical analyses of the relative profitability of retailers and manufacturers.” The empirical studies that have examined this issue have primarily focused on grocery retailers and manufacturers rather than on participants in channels that distribute other types of consumer products. Indeed, the authors of one empirical study addressing this issue stress that nongrocery channels may be different, particularly those where a giant retailer such as Wal-Mart or Toys ‘R’ Us might be present (Ailawadi, Borin & Farris, 1995). In fact, they found evidence that Wal-Mart seems to have become more profitable - and by implication more powerful - in recent years. Unfortunately, their study does not present any evidence about Wal-Mart’s impact on its suppliers. The title of a recent trade press article poses the question: “Should you just say no to Wal-Mart?” (Bowman, 1997) According to this article, many suppliers are reportedly feeling squeezed and pressured by giant retailers into taking expensive actions such as lowering prices, accelerating delivery times, offering special allowances, or carrying extra inventory. Our research seeks to help consumer goods suppliers find an answer to the article’s title question. Specifically, the present study tests for empirical evidence that Wal-Mart is wielding power in ways that hurt or help the financial fortunes of its suppliers. In other words, we ask whether Wal-Mart has squeezed its suppliers into making concessions that have hurt their financial performance? Or, have these suppliers benefited from their association with WalMart, achieving financial results that they might not have achieved by collaborating with other less-powerful retailers? Research on the impact of retailer power could also be helpful to public policy makers. The antitrust enforcement agencies need guidance on whether to pursue more cases like the one the Federal Trade Commission recently decided against Toys ‘R’ Us, where they ruled that the company illegally used its market power to restrict the opportunities of manufacturers to sell to competing retailers (FTC, 1998). The FTC determined that the giant retailer “used its dominant position as a toy distributor to extract agreements from and among toy manufacturers to stop selling to warehouse clubs the same toys that they sold to other toy distributors.” While the Toys ‘R’ Us case was primarily concerned with the impact of retailer power on competition among retailers, the FTC has more recently shown concern about how to treat situations where retailer power might be impacting competition among manufacturers. At a
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recent FTC conference, staff members of the FTC displayed great interest in whether agreements between powerful retailers and manufacturers (e.g., slotting allowances, category management programs) could be exclusionary, foreclosing opportunities for many manufacturers to compete for customers at the retail level (Weir, 2000). Thus, we seek a broader understanding of how the marketing and business practices of this giant retailer affect its suppliers and society. We begin with a brief review of previous research on retailer power and its effects on manufacturers. This is followed by a presentation of the hypotheses examined in our study. The methodologies employed to test the hypotheses are then explained, followed by a report on the results. We conclude with a discussion of the implications of our findings for future marketing practice, academic research, and public policy.
2. Literature A number of streams of research are relevant to our study. These streams explain why squeezing by a retailer may or may not occur plus some relevant empirical evidence. These research streams are labeled and reviewed in the following four subsections. 2.1. Power in channels literature The seminal work on power in channels of distribution by Stern and his colleagues (Stern, 1969; El-Ansary & Stern, 1972; Stern & El-Ansary, 1977; Cadotte & Stern, 1979; Stern & Reve, 1980) and others (Hunt & Nevin, 1974; Lusch, 1976; Frazier, 1983; Gaski, 1984) suggests that one channel member can gain power over another channel member by creating a dependency relationship. To the extent that Wal-Mart’s suppliers have grown dependent on Wal-Mart to keep their sales volumes at certain levels, they could be in an inferior power position to Wal-Mart and subject to being squeezed for financially-damaging concessions. Only if a supplier had some ability to exert “countervailing power” against Wal-Mart –through leveraging a well-known brand name or capitalizing on consumer loyalty –would the power relationship become more balanced (Etgar, 1976; Gaski, 1984). 2.2. Relationship marketing literature Research on relationship marketing suggests that selling to a giant like Wal-Mart may not lead to squeezing –that working in a partnership with large, efficient channel members can bring substantial benefits to both parties (Erdem & Harrison-Walker, 1997; Frazier & Lassar, 1996; Messinger & Narasimhan, 1995; Anderson & Narus, 1991; Anderson, Lodish & Weitz, 1987). Empirical support for the proposition that a strong relationship with powerful channel partners can be profitable comes from the work of Kalwani and Narayandas (1995). These authors used Compustat data to find that suppliers in long-term relationships with manufacturers enjoyed substantially better financial performance than their counterparts not
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in such relationships. But Kalwani and Narayandas did not look at suppliers to retailers, nor did they consider the impact of partnering with a firm as potentially powerful as Wal-Mart. 2.3 Theoretical modeling literature The theoretical and modeling work on channel relationships has identified numerous explanations for why concessions would be offered by a channel member, few of which have anything to do with submitting to a more powerful exchange partner. For example, modeling work on slotting fees has suggested that manufacturers can use these fees as an efficient mechanism for signaling retailers about the quality and likely success of new products (Lariviere & Padmanabhan, 1997; Sullivan, 1997; Desai, 1998). These authors argue that, rather than feeling forced to make concessions to retailers, these manufacturers should feel eager to pay slotting fees to gain access to shelf space and consumers less expensively than by using other promotional tools. In another modeling paper, Kim and Staelin (1998) argue that manufacturers would want to make concessions to help retailers attain more consumer loyalty to their stores versus competing stores. This, in turn, should help manufacturers obtain more brand loyalty and profit than if other promotional tools were used. 2.4. Empirical literature Whether manufacturers are making concessions to the increased power of retailers or are, instead, being highly opportunistic when offering price concessions or accelerating delivery times, can only be determined through empirical research. Recent survey research of retail managers and manufacturing managers in the grocery industry by Bloom, Gundlach & Cannon (2000) suggests, for example, that few managers feel that slotting fees are used as a signal –and that most manufacturer managers and many retailer managers see the fees as an outgrowth of increased retailer power. However, it is possible that managers respond to surveys in one way and actually behave in another. Manufacturing managers may cry extortion when asked about slotting fees, yet pay them gladly as a way to enhance their profitability. Hence, it is useful to look at the actual financial performance of both retailers and manufacturers to obtain a better reading on their true power relationship. Of course, financial performance can also be misleading for studying the impact of power, especially if suppliers are keeping their margins and profits down to be opportunistic. Several studies have compared the financial performance of retailers and manufacturers. For instance, Farris and Ailawadi (1992), using Compustat data on margins, return on sales, and return of assets for the years 1972 to 1990, found no evidence of better relative performance by retailers. If anything, it appeared that grocery manufacturers consistently performed better. This was especially the case for larger manufacturers, but not necessarily for smaller ones. Thus, their work points to the importance of considering how the size of a manufacturer might influence that firm’s ability to thwart the power of a retailer. Messinger and Narasimhan (1995) also looked at margins and return on sales for the years 1961 to 1991 for both grocery retailers and manufacturers, using data obtained from Robert Morris Associates, the Bureau of the Census, the Federal Trade Commission, and a Cornell
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University annual survey. They found no evidence of improved financial performance over time for retailers relative to manufacturers. Instead, the evidence tilted more toward a finding that grocery manufacturers improved their financial picture better than did grocery retailers. In another study, Ailawadi, Borin & Farris (1995) also found that the financial performance of grocery retailers had not improved relative to grocery manufacturers in the 1982 to 1992 time period. In fact, grocery manufacturers were found to have improved their financial performance much more than retailers during this era, even when considering more sophisticated measures of financial performance that adjusted for differing costs of capital. Ailawadi, Borin, and Farris additionally found little evidence that retailers in other nongrocery consumer goods industries performed better than manufacturers during this time period, but found this only when they removed the effect of Wal-Mart from the analysis. They found that Wal-Mart –and to a lesser extent Toys ‘R’ Us and Home Depot –performed very well during this period, indicating at the very least that they had accumulated enough market power to damage the fortunes of competing retailers. However, these authors did not examine how manufacturers that dealt with Wal-Mart fared. Our study examines how Wal-Mart’s suppliers performed using an empirical approach similar to that employed by Kalwani and Narayandas (1995). Like Farris & Ailawadi (1992), we consider how the size of manufacturers can moderate the effects of retailer power.
3. Hypotheses Our review of the literature finds strongly diverse views with respect to whether power has shifted from large, financially successful retailers, such as Wal-Mart, so as to diminish supplier welfare. Wal-Mart has amassed considerable power and may use this to squeeze less powerful manufacturers into making concessions. However, collaborating with Wal-Mart may have helped the financial fortunes of many manufacturers by providing an impetus for more efficiency and improved marketing strategies. A recent article in Forbes magazine (Schifrin, 1996) noted that Wal-Mart has increased its “partnering” arrangements with vendors, and that many of these vendors rely on Wal-Mart for up to 50% of their revenues. According to this account, such companies owe a good deal of their success to Wal-Mart, but are now suffering because Wal-Mart is using its trade leverage to impose tough pricing and merchandising requirements. Plotkin (1997) notes that Wal-Mart was being particularly aggressive against its smaller suppliers. On the other hand, some suppliers may be able to defend themselves against any squeeze attempts by Wal-Mart as they provide critical products and skills. This could happen because of the size, brand equity, market knowledge, or some other characteristic of a manufacturer that would allow it to deploy forms of countervailing power (Etgar, 1976) against Wal-Mart or otherwise win its affection. Additionally, the granting of certain concessions can put a supplier in a favored status with Wal-Mart. Suppliers that form a close relationship with Wal-Mart may benefit from Wal-Mart’s efforts to be efficient and aggressive in attracting consumers to the suppliers’ products. Furthermore, a close relationship between a supplier and Wal-Mart may make it difficult for other suppliers to gain access to Wal-Mart’s shelf space and customers, creating a type
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of entry barrier for those suppliers. Thus, Wal-Mart suppliers might perform better financially than their counterparts that do not sell through Wal-Mart because they may be poised to take advantage of the sales volume, efficiencies, and entry barriers created by having a strong relationship with Wal-Mart. Based on research that has been done on the PIMS data (Buzzell & Gale, 1987), we further expect a supplier with a large-category market share to perform better than smaller share rivals in the same market regardless of what retailers it supplies. To account for this, we control market share effects on profitability as well as annual sales volume, a measure of firm scale. Sales volume could also affect profitability since larger firms (regardless of their market share in their industries) may achieve certain economies of scale and scope. These views dictate that two competing hypotheses be tested: H1a: After controlling for the effects of market share and scale, manufacturers selling a significant portion of their output to Wal-Mart achieve less financial success than their counterparts that: (1) do not sell a significant portion of their output to any single retailer (2) sell a significant portion of their output to retailers other than Wal-Mart. H1b: After controlling for the effects of the market share and scale, manufacturer selling a significant portion of their output to Wal-Mart achieve more financial success than their counterparts that: (1) do not sell a significant portion of their output to a single retailer and (2) sell a significant portion of their output to retailers other than Wal-Mart. Controlling for share and scale does not account for the possibility that Wal-Mart suppliers might benefit financially from having a larger share or scale than would other suppliers. In particular, we find it possible that supplier type could moderate the relationship between the market share of the manufacturer and profitability. We expect this because suppliers are less likely to be squeezed when they become dominant in their markets and more likely to develop efficiencies and stronger sales through partnering with Wal-Mart. Similarly, suppliers who trade with other retailers, large or small, would get squeezed less when they had small shares. Commensurately, they would not benefit as much from their retailer relationships as those who deal with Wal-Mart when they have larger shares. H2: After controlling for the effects of scale, suppliers that sell a significant portion of their output to Wal-Mart will experience a stronger relationship between their category market share and financial success than counterparts that: (1) do not sell a significant portion of their output to a single retailer (2) sell a significant portion of their output to retailers other than Wal-Mart. In addition to the above hypotheses, we also investigate whether suppliers that sell a significant portion of their output to large retailers other than Wal-Mart enjoy different results than their counterparts that do not sell a significant portion of their output to a single
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retailer. Essentially, we explore the same issues for these other suppliers as for Wal-Mart suppliers, but do not present formal hypotheses since our expectations are less strong. In order to test these hypotheses, we use financial performance as an indicator of the relative power a manufacturer holds versus a retailer. This approach is consistent with the work of Farris and others. It would be hard to conclude that Wal-Mart has not grown more powerful than its suppliers if support were found for H1a. Rather than being squeezed by the more powerful firm, however, Wal-Mart suppliers may incur lower performance deliberately in order to take advantage of benefits from working with this large retailer. For examining this issue, future studies may wish to employ survey research to obtain insight as to the strategic purpose of discounts offered.
4. Method Our analysis utilizes Compustat data on publicly owned firms by primary four-digit SIC code, combining the years 1988 to 1994. Although the data span several years, they are cross-sectional rather than a time-series because the same firms are not represented in each year. The names of firms within an industry change from year to year, perhaps reflecting bankruptcies, mergers, or other changes in ownership. Descriptions of the variables, industries, and firms studied are included in Appendixes I through III. Note that the sales, profits, and other dollar amounts from the Compustat database were converted to constant 1984 dollars using the producer price index to adjust for inflation. One key piece of information that we utilize (as did Kalwani and Narayandas 1995) includes the respondent-firm entries of the names of up to four firms they identified as “Primary Customers” stored in the Compustat database. These are customers who accounted for at least 10% of the firm’s annual sales revenue. Only industries in which at least one firm reported Wal-Mart as a primary customer in the year 1994, the last year in our data base, were included. In other words, in order for a firm to qualify for our sample, either Wal-Mart was one of its primary customers, or one of its competitors’ primary customers, during the year 1994. The sample was restricted in this way so that the financial performance of Wal-Mart suppliers would only be compared to the performance of non-Wal-Mart suppliers from similar industries (i.e., four-digit SIC codes). If Wal-Mart was not a primary customer to a supplier in an industry in 1994, then the industry might be so different (e.g., not supplying retailers) that it would make an inappropriate basis for comparison. The data set has one observation for each firm in each reported sales year, resulting in a total of 6,676 firm-year observations. As indicated, firm names vary across the seven years. Four hundred and ninety-eight firms provided data for all the years, but most of these were not Wal-Mart suppliers. We defined a manufacturer to be a Wal-Mart supplier if Wal-Mart appeared anywhere in the primary customer fields. Thus, some of our Wal-Mart suppliers only had Wal-Mart as a primary customer and others had Wal-Mart plus one to three other firms as primary customers. We also identified a group of other manufacturers to be Other Suppliers, meaning that they did not list Wal-Mart as a primary customer, but did list at least one other retail firm as a primary customer. Among the customers listed were Home Shopping Network, K-Mart,
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Radio Shack, Sears, Toys R Us, and the U.S. Department of Defense, as well as other companies that were less obviously involved in retailing. Finally, those manufacturers that did not list any primary customers were defined as Unaffiliated Suppliers. Of course, it is important to note that the primary customer field is optional in the Compustat data. Although we have assumed that a blank in this field indicates that a firm does not have major customers or is unaffiliated, a blank could also mean the firm simply failed to report the information. To the extent that this nonreporting bias exists in the database, it should lead to more similarity between firms we defined as Wal-Mart suppliers and those we defined as Unaffiliated Suppliers. Hence, any nonreporting bias should tend to reduce the magnitude of effects detected from a Wal-Mart connection. To test the hypotheses, the relationship between market share and financial performance was examined for each of the three types of suppliers. Financial performance, ZPROFIT, represents the z-score for net profits obtained by calculating how many standard deviations a given supplier’s net profits was above or below the mean net profits earned in an industry. By using this measure, one can make the analysis across many industries and begin to control for the variation in a supplier’s net profits that might be explained by the uniqueness of markets. We tested the following regression models to provide statistical evidence about the hypotheses: ZPROFIT ⫽ a ⫹ 1WM1 ⫹ 2MKTSHR ⫹ 3WM1*MKTSHR ⫹ 4LNETSALES ⫹
(1)
where: WM1 ⫽ 1 if Wal-Mart is a primary customer, 0 if no primary customers MKTSHR ⫽ ratio of firm’s annual net sales to total industry net sales WM1*MKTSHR ⫽ interaction of Wal-Mart dummy variable and market share LNETSALES ⫽ log of firm net sales ZPROFIT ⫽ a ⫹ 1WM2 ⫹ 2MKTSHR ⫹ 3WM2*MKTSHR ⫹ 4LNETSALES ⫹
(2)
where: WM2 ⫽ 1 when Wal-Mart is a primary customer, 0 if another retailer was a primary customer ZPROFIT ⫽ a ⫹ 1OTHER ⫹ 2MKTSHR ⫹ 3OTHER*MKTSHR ⫹ 4LNETSALES ⫹
(3)
where: OTHER ⫽ 1 if a firm other than Wal-Mart is the primary customer, 0 if no primary customers We employed the first model on a subsample of suppliers that had either identified Wal-Mart or no retailers as a primary customer. The second model was used on a subsample of suppliers that either had identified Wal-Mart as a primary customer or had identified another retailer as a primary customer. The third model was used on suppliers that either had
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another retailer besides Wal-Mart as a primary customer or had identified no retailer as a primary customer. The coefficient on WM1 in model 1 determines whether having Wal-Mart as a primary customer hurts or helps a supplier’s profitability as compared to unaffiliated suppliers (i.e., test H1a(1) versus H1b(1)). Similarly, we use the coefficient on WM2 in model 2 to determine whether having Wal-Mart as a major customer hurts or helps a company’s profitability compared to suppliers of other companies (i.e., test H1a(2) versus H1b(2)). We use the coefficient for Other in model 3 to determine whether supplier profits are hurt or helped when they have major commitments to such retailers as compared to when they do not (unaffiliated). In all models, the log of net sales was used to control for scale. Similarly, supplier market share was employed to control for upstream market power. We control for market share in all three models by including this variable plus an interaction term using dummy variables. The market share interaction in models 1 and 2 determines whether the Wal-Mart relationship boosts the profit they obtain from market share. As noted earlier, if the working relationship with Wal-Mart provides the supplier with greater efficiencies, such suppliers should enjoy greater profits from higher market share than others not so affiliated (i.e., testing H2(1) and H2(2)). In order to control for variation in economic conditions across years, a dummy variable for each sales year was included in a first regression model (YR88-YR94). The year 1994 served as the omitted category. A similar approach has been used frequently in econometrics studies (Neter et al.,1990, p.375). However, the results from this approach were not substantively different from those achieved with the simpler models and are not reported.
5. Results The results of the regressions are reported in Table 1. Not surprisingly, they reflect higher supplier profits from both greater category market share and from greater scale of operation. This pattern appears strongly across all three of the models. The coefficients on both WM1 and WM2 are significant and negative. This indicates that when Wal-Mart is a primary customer that profits fall for suppliers regardless of size. Wal-Mart, in other words, takes a bite in exchange for being a major customer for the supplier. This supports H1a, as opposed to H1b, indicating that Wal-Mart suppliers are squeezed financially more than their counterparts, ceteris paribus . However, at the same time, the positive coefficient on the interaction terms in the first two models suggests that Wal-Mart suppliers obtain greater profit from their market share than do their counterparts. This is commensurate with both parts of H2. While the data do not permit the calculation of the trade-off between joining the Wal-Mart team, this second finding suggests that where Wal-Mart suppliers obtain larger market shares, profitability is greater than for their counterparts. The results for the third model indicate a difference between supplier relationships with other retailers as compared to Wal-Mart. Here, we find that smaller retailers do not take the bite that Wal-Mart does, as the dummy coefficient for Other was not significant. We also see,
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Table 1 Regression Results Z-Profit is Dependent Variable Ind. Vars.\Sample
WM vs. No-Primary
WM vs. Other Primary
Other Primary vs. No Primary
Intercept MarketShare Log(Netsales) WM1 WM1*MktShare WM2 WM2*MktShare Other Primary Rtlr. Other*MktShare Rtlr. R2 Adj. R2 Sample Size
⫺0.78*** 1.19*** 0.16*** ⫺0.24*** 0.46*
⫺0.66*** 1.12*** 0.12***
⫺0.72*** 1.25*** 0.15***
0.35 0.35 3,962
⫺0.20*** 0.66*** 0.33 0.33 2,931
⫺0.02 ⫺0.25*** 0.34 0.34 6,598
*** Significant at 0.01 level ** Significant at 0.05 level * Significant at 0.10 level Sub-Samples & Variable Definitions: Sub-sample includes suppliers of Wal-Mart and retailers not identified as primary customers. WM1 ⫽ Wal-Mart dummy variable ⫽ 1 Sub-sample includes only suppliers reporting a primary retailer, including Wal-Mart and all others so designated. WM2 ⫽ Wal-Mart dummy variable ⫽ 1; Sub-sample includes suppliers with other primary retail customers and those with no primary retail customers. Other ⫽ other primary retailer dummy variable ⫽ 1.
and surprisingly, that efficiencies in supplier operations are not derived from major relationships with these other retailers. The Other interaction coefficient is significantly negative. This indicates that as suppliers to Other retailers increase their market share, they actually lose profitability more rapidly than is the case for unaffiliated suppliers. 5.1. Significance We ran several tests to determine whether the differences between the dummy variable coefficients (Wal-Mart, Other, Unaffiliated) were significant. In other words, we tested for differences in the intercepts due to a firm being a supplier of Wal-Mart, another primary customer or because the firm has no primary customers. We ran a regression model including the entire sample (all three supplier types), and the set of independent variables, including interactions, used in the three models described above. Using these estimates, we used the F-test to determine whether the coefficient on the Wal-Mart dummy variable was equal to the coefficient on the Other dummy variable. The results of this test suggest that these coefficients are significantly different from each other. We made similar F tests to determine whether the coefficient on Wal-Mart was significantly different from the coefficient on the intercept, which in this case represents the omitted, unaffiliated supplier category. Again, the test suggests that these coefficients are significantly different. We ran additional coefficient tests for significant differences between Other and
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Table 2 Pearson Correlation Coefficients Prob ⬎ [R] under 0 N ⫽ 6,676 WALMART
WALMART 1.000 Prob. 0.000 OTHER 0.079 Prob. 0.000 ZPROF ⫺0.003 Prob. 0.831 MKTSHR 0.041 Prob. 0.001 WALMART*MKTSHR 0.550 Prob. 0.000 OTHER*MKTSHR 0.093 Prob. 0.000 LOG OF NETSALES 0.033 Prob. 0.006
OTHER Z-PROFIT MKTSHR WALMART* OTHER* LOG OF MKTSHR MKTSHR NET SALES 0.079 ⫺0.003 0.000 0.831 1.000 ⫺0.100 0.000 0.000 ⫺0.100 1.000 0.000 0.000 ⫺0.059 0.440 0.000 0.000 0.055 0.086 0.000 0.000 0.359 0.192 0.000 0.000 ⫺0.138 0.526 0.000 0.000
0.041 0.001 ⫺0.059 0.000 0.440 0.000 1.000 0.000 0.194 0.000 0.546 0.000 0.396 0.000
0.550 0.000 0.055 0.000 0.086 0.000 0.194 0.000 1.000 0.000 0.260 0.000 0.066 0.000
0.093 0.000 0.359 0.000 0.192 0.000 0.546 0.000 0.260 0.000 1.000 0.000 0.179 0.000
0.033 0.008 ⫺0.138 0.000 0.526 0.000 0.396 0.000 0.066 0.000 0.179 0.000 1.000 0.000
the Intercept (the omitted Unaffiliated category), and for significant differences between the interaction terms (Wal-Mart*Mktshr and Other*Mktshr). In each case, the F tests indicate that these pairs of variables are significantly different from each other. Thus, we conclude that Wal-Mart’s effect on supplier profits is significantly different than the effects due to dealing with other major retailers. In addition, having higher market share combined with having a relationship with Wal-Mart has a significantly different effect on supplier profits than having a relationship with another retailer while having high market share. 5.2. Multicollinearity Table 2 presents the correlation matrix for all variables in the pooled sample of Wal-Mart suppliers, Other Suppliers, and Unaffiliated Suppliers. In order to check for multicollinearity between independent variables in our regression models, we computed variance inflation factors. These statistics show how multicollinearity causes instability in the coefficient estimates. Variance inflation factors higher than 10 are commonly considered to be cause for concern. In these three regression models, all variance inflation factors were less than 2.0. Another way to assess multicollinearity is to examine the eigenvalues of the correlation matrix of the set of independent variables in each model. Very small eigenvalues, or large variability among eigenvalues, suggests that multicollinearity may be a problem. The condition number, or the square root of the ratio of the largest to smallest eigenvalue, reflects this variability. Values greater than 30 are considered to be indicative of multicollinearity. Out of our three models, the highest condition number was 4.96. Based on these results, we conclude that multicollinearity is not affecting the results of our regression analyses.
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Table 3 Descriptive Statistics on Size and Profit Measures by Supplier Type Wal-Mart Suppliers
Net Sales* Log of Net Sales Net Profit* Net Profit Z-Score Market Share *
Other Suppliers
Mean
Std Dev
Med
Mean
Std Dev
366.93 4.70
583.87 1.68
104.91 4.65
419.79 3.59
1643.02 2.34
42.14 0.12
77.98 1.01
6.69 ⫺0.33
34.12 ⫺0.11
125.09 0.77
0.14
0.20 N ⫽ 78
0.04
0.10
0.20 N ⫽ 2853
Unaffiliated Suppliers Med
Mean
Std Dev
Med
33.91 3.52
884.15 4.31
2595.64 2.76
101.41 4.62
1.93 ⫺0.34
95.24 0.08
280.89 1.04
7.08 ⫺0.31
0.01
0.13
0.23 N ⫽ 3745
0.02
in millions of dollars
6. Discussion Wal-Mart suppliers in our database are relatively smaller than their counterparts in terms of sales. Table 3 provides descriptive statistics about the Wal-Mart, other, and Unaffiliated Suppliers. As smaller entities, they earn lower profits than larger entities and it may be possible to attribute this poorer performance to their connection to Wal-Mart. The regression results indicate that the presence of Wal-Mart as a primary customer hurts the financial performance of a supplier, especially when the supplier has a small market share. This supports Plotkin’s position (Plotkin, 1997) that small suppliers do not fare well with Wal-Mart. However, the data also indicate that a Wal-Mart relationship may help a supplier when it obtains a large market share, especially if this is accomplished with Wal-Mart’s help. It is nevertheless instructive to compare Wal-Mart supplier performance against suppliers with major relationships with other retailers. Here, we find that despite its bargaining strength, Wal-Mart may offer a better relationship than its smaller retail competitors. The market share and the interaction market share coefficients are superior for the comparison of Wal-Mart suppliers to unaffiliated suppliers as compared to the coefficients for the comparison of suppliers to other retailers to unaffiliated suppliers. Conceivably these weaker (negatively-signed) results for suppliers with major commitments to Other retailers could be explained by the failure of the unaffiliated suppliers to complete the item on the Compustat questionnaire when they actually were Wal-Mart suppliers. The better-performing, larger-share, unaffiliated suppliers could actually include many Wal-Mart suppliers if this were the case. However, it may also be the case that Wal-Mart simply does a better job of forging relationships that permit its suppliers to be more efficient when they get large. Or, larger-share suppliers that distribute primarily through retailers other than Wal-Mart may be at a disadvantage and perform poorly when forced to compete with larger-share rivals that have strong Wal-Mart connections. Unfortunately, our data did not allow us to track more than a few individual firms over time to see if they improved their market shares or efficiency while selling to Wal-Mart. Such
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a tracking study would help determine whether the impact of the dynamics of a Wal-Mart relationship upon a supplier’s profits. Another direction for such tracking studies that could be helpful to public policy makers would be to determine whether suppliers to Wal-Mart are able to leverage their relationship to increase their market shares. Their exposure with Wal-Mart may provide improved recognition of their brand. It may also permit them to exclude competitors from shelf space and to extend market share. As discussed earlier, the antitrust enforcement agencies have become interested in whether agreements between suppliers and retailers lead to exclusion of competing suppliers.
7. Conclusion Our results show that it is not possible to identify the impact of Wal-Mart upon supplier profits unambiguously. Ceteris paribus, we find that suppliers that identify Wal-Mart as a primary customer perform more poorly financially than those that do not identify themselves in this way. But, these results do not suggest that suppliers “Should you just say no to Wal-Mart?” While Wal-Mart may be using its power to squeeze suppliers, it is also possible that suppliers are willing to make concessions in the hope that a Wal-Mart relationship will help them expand their market share. Further, our data suggest that developing a relationship with Wal-Mart could lead to a boon rather than a bust for the astute manufacturer, once a certain market share is reached. Large-share suppliers to Wal-Mart extract more profits from their market share than do their counterparts without such a relationship. This raises another question of interest. Could Wal-Mart leave this money on the table deliberately in order to attract better suppliers?
Acknowledgement The views expressed in this paper do not represent the views of Freddie Mac, its management or shareholders. The authors are deeply appreciative of the comments provided by Charlotte Mason, the Journal of Retailing reviewers, and the editor, L. P. Bucklin.
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Appendix I - List of Compustat Standard Industry Classification (SICs) that include Wal-Mart Supplier Firms SIC Code
Name
SIC Code
Name
2253
APPAREL
2387
2899 2066
CHEMICAL PRODUCTS CONFECTIONERY
3944 2834
2731 3634 2879 3663 3469
CONSUMER PRODUCTS CONSUMER PRODUCTS & SERVICES DISINFECTANTS ELECTR COMMUNICATIONS SYS FABRICATED METAL PRODUCTS
2369 3842 2325 2321 3911
2676
FEMININE HYGIENE & CONTRCPTIVES
3751
3861
FILM PROCESSORS
3579
3089
FLOORING PRODUCTS
3827
3142
FOOTWEAR
2782
2844 2329
FRAGRANCES & COSMETICS GOLF APPAREL
2392 2679
2771
3524
3171
GREETING CARDS & RELATED PRODUCTS HANDBAGS & APPAREL ITEMS
3635
HOME CLEANING SYSTEMS
2393
2842 3365 3080
HOUSEHOLD GROCERY PRODUCTS HOUSEWARES INFANT & CHILD PRODUCTS
3651 3949 3661
2399 3812 3961 3645
INFANT PRODUCTS INSTRUMENT PRODUCTS JEWELRY LAMPS
2211 3942 2339 2326
LEATHER GOODS & ACCESSORIES LEISURE PRODUCTS MAMMALIAN CELL CULTURE SYS MANUFACTURING MECHANICAL HEART VALVE MENS APPAREL MENS SPORTSWEAR MOTIVATION-RECOGNITION PRODUCTS MOTORCYCLES & RELATED PRODUCTS OFFICE & GRAPHIC ARTS PRODUCTS OPTICAL COMPONENTS & SYSTEMS PERSONAL ORGANIZERSPLANNERS PILLOWS & BEDDING PRODUCT IDENTIFICATION SYSTEMS PROFESSIONAL-GARDENLEISURE PRODUCTS ROOFTOP AIR-CONDITIONING & HEATING EQUIPMENT SMOCK & CLOTH BAG MANUFACTURING SPEAKER SYSTEMS SPORTS ACCESSORIES TELECOMM ACCESS & TEST PRODUCTS TEXTILE MILL PRODUCTS TOYS WOMENS APPAREL WORK APPAREL
3585
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393
Appendix II - Top 25 Wal-Mart Suppliers in the Compustat Database*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Company Name
Net Sales in millions of dollars
HERSHEY FOODS CORP POLAROID CORP NEWELL RUBBERMAID INC CLOROX COMPANY SPRINGS INDUSTRIES SUNBEAM CORPORATION RUSSELL CORP PARAGON TRADE BRANDS INC SMITH CORONA CORP EKCO GROUP INC OROAMERICA INC MCKESSON HBOC INC CROWN CRAFTS INC WINDMERE-DURABLE HOLDINGS DEL LABORATORIES INC NATIONAL PRESTO INDUSTRIES INC TANDY BRANDS ACCESSORIES INC SCOTT’S LIQUID GOLD NOEL GROUP INC DEL LABORATORIES INC PREMIUMWEAR INC HOME PRODUCTS INT’L INC DAKOTAH INC L A T SPORTSWEAR INC AJAY SPORTS INC
2987.80 1915.91 1719.08 1529.25 1209.69 992.88 909.91 479.39 230.85 221.25 181.79 179.62 174.78 150.05 117.39 106.11 56.71 44.25 43.06 40.11 34.74 29.66 25.19 19.98 10.69
* Based on 1994 annual net sales volume.
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Appendix III - Top 25 Suppliers to Non-Wal-Mart (Other) Primary Customers in the Compustat Database*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Company Name
Net Sales in millions of dollars
NEC CORP CANON INC NORTEL NETWORKS CORP BCE INC WHIRLPOOL CORP THOMSON CSF GENERAL MOTORS GENERAL MOTORS CL H ROCKWELL INTL CORP NIKE INC ALLIED SIGNAL INC RAYTHEON CO LOCKHEED MARTIN CORP REEBOK INTERNATIONAL LTD ALLIED SIGNAL INC UNITED TECHNOLOGIES CORP MATTEL INC LEAR CORP FORT JAMES CORP ROCKWELL INTL CORP HASBRO INC CBS CORP YORK INTL EASTMAN CHEMICAL CO TENNECO INC
35895.53 13579.70 7368.68 7167.57 6585.75 5640.59 4614.00 4321.87 4154.93 3944.35 3830.16 3370.34 3359.57 2717.83 2710.85 2666.11 2655.36 2607.71 2529.88 2341.34 2212.31 2043.91 2006.52 1958.57 1809.44
* Based on 1994 annual net sales volume.
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References Ailawadi, K. L. (2001). The Retail Power-Performance Conundrum: What Have We Learned? Journal of Retailing, forthcoming. Ailawadi, K. L., Borin, N., & Farris, P. W. (1995). Market Power and Performance: A Cross-Industry Analysis of Manufacturers and Retailers,“ Journal of Retailing, 71(3), 211–248. Anderson, E., Lodish, L. M., & Weitz, B. A. (1987). Resource Allocation Behavior in Conventional Channels,“ Journal of Marketing Research, 24 (February), 85–97. Anderson, J. C., & Narus, J. A. (1991). Partnering as a Focused Market Strategy, California Management Review, 33(Spring), 95–113. Bloom, P. N., Gundlach, G. T., & Cannon, J. P. (1998). Slotting Allowances and Fees: Schools of Thought and the Views of Practicing Managers. Journal of Marketing, 64(April): 92–108. Bowman, R. J. (1997). Should You Just Say No to Wal-Mart? Distribution, 96(12), 52–54. Buzzell, R. D., & Gale, B. T. (1987). The PIMS Principles: Linking Strategy to Performance. New York: The Free Press. Buzzell, R. D., Quelch, J. A., & Salmon, W. J. (1990). The Costly Bargain of Trade Promotion. Harvard Business Review, 68 (March-April), 141–149. Cadotte, E. R., & Stern, L. W. (1979). A Process Model of Interorganizational Relations in Marketing Channels. In Jagdesh Sheth (Ed.), Research in Marketing: Volume 2, (pp. 127–158). Greenwich, CT: JAI Press. Desai, P. S. (1998). Multiple Messages to Retain Retailers: Signaling New Product Demand, working paper, Fuqua School of Business, Duke University. El-Ansary, A. I., & Stern, L. W. (1972). Power Measurement in the Distribution Channel. Journal of Marketing Research, 9(1), 47–52. Elman, D., & Hughes, B. (1988). How Much are Deals Driving BM/HBA? Supermarket Business, (April), 57– 60. Erdem, S. A., & Harrison-Walker, L. J. (1997). Managing Channel Relationships: Toward an Identification of Effective Promotional Strategies in Vertical Marketing Systems, Journal of Marketing Theory and Practice, 5 (Spring), 80 – 87. Etgar, M. (1976). Channel Domination and Countervailing Power in Distributive Channels. Journal of Marketing Research, 13(3), 254 –262. Farris, P. W., & Ailawadi, K. L. (1992). Retail Power: Monster or Mouse? Journal of Retailing, 68(4), 351–369. Frazier, G. L. (1983). Interorganizational Exchange Behavior in Marketing Channels: A Broadened Perspective. Journal of Marketing, 47(4), 68 –78. Frazier, G. L., & Lassar, W. M. (1996). Determinants of Distribution Intensity. Journal of Marketing, 60(4), 39 –51. Federal Trade Commission (1998). FTC Upholds Charges the Toys ‘R’ Us Induced Toy Makers to Stop Selling Desirable Toys to Warehouse Clubs, FTC News Release, October 14. Gaski, J. F. (1984). The Theory of Power and Conflict in Channels of Distribution. Journal of Marketing, 48(3), 9 –29. Hunt, S. D., & Nevin, J. R. (1974). Power in a Channel of Distribution: Sources and Consequences. Journal of Marketing Research, 11(2), 186 –193. Johnson, W. C. (1988). Sales Promotion: It’s Come Down to “Push Marketing”. Marketing News (February 29): 8. Kalwani, M. U., & Narayandas, N. (1995). Long-Term Manufacturer-Supplier Relationships: Do They Pay Off for Supplier Firms? Journal of Marketing, 59 (January): 1–16. Kim, S. Y., & Staelin, R. (1998). “Retail Power: Is it an Illusion?” working paper, Fuqua School of Business, Duke University. Lariviere, M. A., & Padmanabhan, V. (1997). Slotting Allowances and New Product Introductions. Marketing Science, 16(2), 112–128. Lusch, R. F. (1976). Sources of Power: Their Impact on Intrachannel Conflict. Journal of Marketing Research, 13(4), 382–390.
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P.N. Bloom, V.G. Perry / Journal of Retailing 77 (2001) 379 –396
Messinger, P. R., & Narasimhan, C. (1995). Has Power Shifted in the Grocery Channel? Marketing Science, 14, 189 –223. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied Linear Statistical Models: 3rd Edition. Burr Ridge, IL: Richard D. Irwin. Plotkin, H. (1997). Wal-Mart Throws the Book at Small-Biz Vendors. Inc. Magazine, (January). Schifrin, M. (1996). The Big Squeeze. Forbes, March 11, 1996, 45– 46. Stern, L. W., ed. (1969). Distribution Channels: Behavioral Dimensions. Boston: Houghton Mifflin. Stern, L. W., El-Ansary, A. I. (1977). Marketing Channels. Englewood Cliffs, NJ: Prentice-Hall. Stern, L. W., & Eovaldi, T. L. (1984). Legal Aspects of Marketing Strategy: Antitrust and Consumer Protection Issues. Englewood Cliffs, NJ: Prentice-Hall. Stern, L. W., & Reve, T. (1980), Distribution Channels as Political Economies: A Framework for Comparative Analysis. Journal of Marketing, 44(Summer), 52– 64. Sullivan, M. W. (1997). Slotting Allowances and the Market for New Products. Journal of Law and Economics, 40(October), 461– 493. Weir, T. (2000). Slotting in the Spotlight. Supermarket Business, 55(7), 1–14.
Retailer power and supplier welfare: The case of Wal-Mart Paul N. Blooma,*, Vanessa G. Perryb a
Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC 27599-3490, USA George Washington University, School of Business and Public Management, Washington, DC 20052
b
Received 2 April 1999; accepted 21 April 2001
Abstract Whether retailers have become more powerful than manufacturers in recent years continues to be a burning question in the trade press and academic literature. Our research adds fresh fuel to the fire by looking at whether Wal-Mart, the largest retailer in the United States, has exerted power over its suppliers and squeezed them financially. Previous academic research on retailer power has looked largely at food stores, but we extend this perspective into nonfoods by using Compustat data as a source. Our analysis of these data indicates that the answer may be more complex than a simple yes or no. We find that Wal-Mart suppliers holding a small share of their respective markets do not perform relatively as well financially when they have Wal-Mart as one of their primary customers. However, large-share suppliers to Wal-Mart perform better than their large-share counterparts reporting retailers other than Wal-Mart as their primary customers. This indicates that suppliers who seek Wal-Mart’s wide market reach may derive benefits from using this association if it can be used to strengthen their market positions. Those that fail in this goal, however, may find their profits squeezed and do better by shifting their retail channel focus elsewhere. © 2001 by New York University. All rights reserved.
1. Introduction There has been considerable debate in the trade press and academic literature over whether a significant shift has taken place in the relative power of retailers and manufacturers of consumer products. In general, the trade press has suggested that retailers are increasing their relative power, using it to extract concessions from manufacturers such as merchandising
* Corresponding author. Tel.: ⫹1-919-962-3222; fax: ⫹1-919-962-7186. E-mail address: [email protected] (P.N. Bloom), [email protected] (V.G. Perry). 0022-4359/01/$ – see front matter © 2001 by New York University. All rights reserved. PII: S 0 0 2 2 - 4 3 5 9 ( 0 1 ) 0 0 0 4 8 - 3
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support, trade deals, and slotting allowances (Johnson, 1988; Elman & Hughes, 1988; Buzzell, Quelch & Salmon, 1990; Bowman, 1997). The academic literature, however, has yet to provide conclusive evidence that such a shift has occurred. Several researchers have posed theoretical challenges to the existence of such a shift (Kim & Staelin, 1998; Lariviere & Padmanabhan, 1997; Sullivan, 1997). Moreover, empirical academic work has not shown any significant or consistent improvement in retailer financial performance relative to manufacturers in recent times, which one would expect as a result of a power shift (Farris & Ailawadi, 1992; Messinger & Narasimhan, 1995; Ailawadi, Borin & Farris, 1995). In her review of the work on retailer power, appearing concurrently in this journal, Ailawadi (2001) concludes that: “The conventional wisdom that retailers have grown more powerful relative to packaged goods manufacturers in the packaged goods industry has not been supported by empirical analyses of the relative profitability of retailers and manufacturers.” The empirical studies that have examined this issue have primarily focused on grocery retailers and manufacturers rather than on participants in channels that distribute other types of consumer products. Indeed, the authors of one empirical study addressing this issue stress that nongrocery channels may be different, particularly those where a giant retailer such as Wal-Mart or Toys ‘R’ Us might be present (Ailawadi, Borin & Farris, 1995). In fact, they found evidence that Wal-Mart seems to have become more profitable - and by implication more powerful - in recent years. Unfortunately, their study does not present any evidence about Wal-Mart’s impact on its suppliers. The title of a recent trade press article poses the question: “Should you just say no to Wal-Mart?” (Bowman, 1997) According to this article, many suppliers are reportedly feeling squeezed and pressured by giant retailers into taking expensive actions such as lowering prices, accelerating delivery times, offering special allowances, or carrying extra inventory. Our research seeks to help consumer goods suppliers find an answer to the article’s title question. Specifically, the present study tests for empirical evidence that Wal-Mart is wielding power in ways that hurt or help the financial fortunes of its suppliers. In other words, we ask whether Wal-Mart has squeezed its suppliers into making concessions that have hurt their financial performance? Or, have these suppliers benefited from their association with WalMart, achieving financial results that they might not have achieved by collaborating with other less-powerful retailers? Research on the impact of retailer power could also be helpful to public policy makers. The antitrust enforcement agencies need guidance on whether to pursue more cases like the one the Federal Trade Commission recently decided against Toys ‘R’ Us, where they ruled that the company illegally used its market power to restrict the opportunities of manufacturers to sell to competing retailers (FTC, 1998). The FTC determined that the giant retailer “used its dominant position as a toy distributor to extract agreements from and among toy manufacturers to stop selling to warehouse clubs the same toys that they sold to other toy distributors.” While the Toys ‘R’ Us case was primarily concerned with the impact of retailer power on competition among retailers, the FTC has more recently shown concern about how to treat situations where retailer power might be impacting competition among manufacturers. At a
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recent FTC conference, staff members of the FTC displayed great interest in whether agreements between powerful retailers and manufacturers (e.g., slotting allowances, category management programs) could be exclusionary, foreclosing opportunities for many manufacturers to compete for customers at the retail level (Weir, 2000). Thus, we seek a broader understanding of how the marketing and business practices of this giant retailer affect its suppliers and society. We begin with a brief review of previous research on retailer power and its effects on manufacturers. This is followed by a presentation of the hypotheses examined in our study. The methodologies employed to test the hypotheses are then explained, followed by a report on the results. We conclude with a discussion of the implications of our findings for future marketing practice, academic research, and public policy.
2. Literature A number of streams of research are relevant to our study. These streams explain why squeezing by a retailer may or may not occur plus some relevant empirical evidence. These research streams are labeled and reviewed in the following four subsections. 2.1. Power in channels literature The seminal work on power in channels of distribution by Stern and his colleagues (Stern, 1969; El-Ansary & Stern, 1972; Stern & El-Ansary, 1977; Cadotte & Stern, 1979; Stern & Reve, 1980) and others (Hunt & Nevin, 1974; Lusch, 1976; Frazier, 1983; Gaski, 1984) suggests that one channel member can gain power over another channel member by creating a dependency relationship. To the extent that Wal-Mart’s suppliers have grown dependent on Wal-Mart to keep their sales volumes at certain levels, they could be in an inferior power position to Wal-Mart and subject to being squeezed for financially-damaging concessions. Only if a supplier had some ability to exert “countervailing power” against Wal-Mart –through leveraging a well-known brand name or capitalizing on consumer loyalty –would the power relationship become more balanced (Etgar, 1976; Gaski, 1984). 2.2. Relationship marketing literature Research on relationship marketing suggests that selling to a giant like Wal-Mart may not lead to squeezing –that working in a partnership with large, efficient channel members can bring substantial benefits to both parties (Erdem & Harrison-Walker, 1997; Frazier & Lassar, 1996; Messinger & Narasimhan, 1995; Anderson & Narus, 1991; Anderson, Lodish & Weitz, 1987). Empirical support for the proposition that a strong relationship with powerful channel partners can be profitable comes from the work of Kalwani and Narayandas (1995). These authors used Compustat data to find that suppliers in long-term relationships with manufacturers enjoyed substantially better financial performance than their counterparts not
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in such relationships. But Kalwani and Narayandas did not look at suppliers to retailers, nor did they consider the impact of partnering with a firm as potentially powerful as Wal-Mart. 2.3 Theoretical modeling literature The theoretical and modeling work on channel relationships has identified numerous explanations for why concessions would be offered by a channel member, few of which have anything to do with submitting to a more powerful exchange partner. For example, modeling work on slotting fees has suggested that manufacturers can use these fees as an efficient mechanism for signaling retailers about the quality and likely success of new products (Lariviere & Padmanabhan, 1997; Sullivan, 1997; Desai, 1998). These authors argue that, rather than feeling forced to make concessions to retailers, these manufacturers should feel eager to pay slotting fees to gain access to shelf space and consumers less expensively than by using other promotional tools. In another modeling paper, Kim and Staelin (1998) argue that manufacturers would want to make concessions to help retailers attain more consumer loyalty to their stores versus competing stores. This, in turn, should help manufacturers obtain more brand loyalty and profit than if other promotional tools were used. 2.4. Empirical literature Whether manufacturers are making concessions to the increased power of retailers or are, instead, being highly opportunistic when offering price concessions or accelerating delivery times, can only be determined through empirical research. Recent survey research of retail managers and manufacturing managers in the grocery industry by Bloom, Gundlach & Cannon (2000) suggests, for example, that few managers feel that slotting fees are used as a signal –and that most manufacturer managers and many retailer managers see the fees as an outgrowth of increased retailer power. However, it is possible that managers respond to surveys in one way and actually behave in another. Manufacturing managers may cry extortion when asked about slotting fees, yet pay them gladly as a way to enhance their profitability. Hence, it is useful to look at the actual financial performance of both retailers and manufacturers to obtain a better reading on their true power relationship. Of course, financial performance can also be misleading for studying the impact of power, especially if suppliers are keeping their margins and profits down to be opportunistic. Several studies have compared the financial performance of retailers and manufacturers. For instance, Farris and Ailawadi (1992), using Compustat data on margins, return on sales, and return of assets for the years 1972 to 1990, found no evidence of better relative performance by retailers. If anything, it appeared that grocery manufacturers consistently performed better. This was especially the case for larger manufacturers, but not necessarily for smaller ones. Thus, their work points to the importance of considering how the size of a manufacturer might influence that firm’s ability to thwart the power of a retailer. Messinger and Narasimhan (1995) also looked at margins and return on sales for the years 1961 to 1991 for both grocery retailers and manufacturers, using data obtained from Robert Morris Associates, the Bureau of the Census, the Federal Trade Commission, and a Cornell
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University annual survey. They found no evidence of improved financial performance over time for retailers relative to manufacturers. Instead, the evidence tilted more toward a finding that grocery manufacturers improved their financial picture better than did grocery retailers. In another study, Ailawadi, Borin & Farris (1995) also found that the financial performance of grocery retailers had not improved relative to grocery manufacturers in the 1982 to 1992 time period. In fact, grocery manufacturers were found to have improved their financial performance much more than retailers during this era, even when considering more sophisticated measures of financial performance that adjusted for differing costs of capital. Ailawadi, Borin, and Farris additionally found little evidence that retailers in other nongrocery consumer goods industries performed better than manufacturers during this time period, but found this only when they removed the effect of Wal-Mart from the analysis. They found that Wal-Mart –and to a lesser extent Toys ‘R’ Us and Home Depot –performed very well during this period, indicating at the very least that they had accumulated enough market power to damage the fortunes of competing retailers. However, these authors did not examine how manufacturers that dealt with Wal-Mart fared. Our study examines how Wal-Mart’s suppliers performed using an empirical approach similar to that employed by Kalwani and Narayandas (1995). Like Farris & Ailawadi (1992), we consider how the size of manufacturers can moderate the effects of retailer power.
3. Hypotheses Our review of the literature finds strongly diverse views with respect to whether power has shifted from large, financially successful retailers, such as Wal-Mart, so as to diminish supplier welfare. Wal-Mart has amassed considerable power and may use this to squeeze less powerful manufacturers into making concessions. However, collaborating with Wal-Mart may have helped the financial fortunes of many manufacturers by providing an impetus for more efficiency and improved marketing strategies. A recent article in Forbes magazine (Schifrin, 1996) noted that Wal-Mart has increased its “partnering” arrangements with vendors, and that many of these vendors rely on Wal-Mart for up to 50% of their revenues. According to this account, such companies owe a good deal of their success to Wal-Mart, but are now suffering because Wal-Mart is using its trade leverage to impose tough pricing and merchandising requirements. Plotkin (1997) notes that Wal-Mart was being particularly aggressive against its smaller suppliers. On the other hand, some suppliers may be able to defend themselves against any squeeze attempts by Wal-Mart as they provide critical products and skills. This could happen because of the size, brand equity, market knowledge, or some other characteristic of a manufacturer that would allow it to deploy forms of countervailing power (Etgar, 1976) against Wal-Mart or otherwise win its affection. Additionally, the granting of certain concessions can put a supplier in a favored status with Wal-Mart. Suppliers that form a close relationship with Wal-Mart may benefit from Wal-Mart’s efforts to be efficient and aggressive in attracting consumers to the suppliers’ products. Furthermore, a close relationship between a supplier and Wal-Mart may make it difficult for other suppliers to gain access to Wal-Mart’s shelf space and customers, creating a type
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of entry barrier for those suppliers. Thus, Wal-Mart suppliers might perform better financially than their counterparts that do not sell through Wal-Mart because they may be poised to take advantage of the sales volume, efficiencies, and entry barriers created by having a strong relationship with Wal-Mart. Based on research that has been done on the PIMS data (Buzzell & Gale, 1987), we further expect a supplier with a large-category market share to perform better than smaller share rivals in the same market regardless of what retailers it supplies. To account for this, we control market share effects on profitability as well as annual sales volume, a measure of firm scale. Sales volume could also affect profitability since larger firms (regardless of their market share in their industries) may achieve certain economies of scale and scope. These views dictate that two competing hypotheses be tested: H1a: After controlling for the effects of market share and scale, manufacturers selling a significant portion of their output to Wal-Mart achieve less financial success than their counterparts that: (1) do not sell a significant portion of their output to any single retailer (2) sell a significant portion of their output to retailers other than Wal-Mart. H1b: After controlling for the effects of the market share and scale, manufacturer selling a significant portion of their output to Wal-Mart achieve more financial success than their counterparts that: (1) do not sell a significant portion of their output to a single retailer and (2) sell a significant portion of their output to retailers other than Wal-Mart. Controlling for share and scale does not account for the possibility that Wal-Mart suppliers might benefit financially from having a larger share or scale than would other suppliers. In particular, we find it possible that supplier type could moderate the relationship between the market share of the manufacturer and profitability. We expect this because suppliers are less likely to be squeezed when they become dominant in their markets and more likely to develop efficiencies and stronger sales through partnering with Wal-Mart. Similarly, suppliers who trade with other retailers, large or small, would get squeezed less when they had small shares. Commensurately, they would not benefit as much from their retailer relationships as those who deal with Wal-Mart when they have larger shares. H2: After controlling for the effects of scale, suppliers that sell a significant portion of their output to Wal-Mart will experience a stronger relationship between their category market share and financial success than counterparts that: (1) do not sell a significant portion of their output to a single retailer (2) sell a significant portion of their output to retailers other than Wal-Mart. In addition to the above hypotheses, we also investigate whether suppliers that sell a significant portion of their output to large retailers other than Wal-Mart enjoy different results than their counterparts that do not sell a significant portion of their output to a single
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retailer. Essentially, we explore the same issues for these other suppliers as for Wal-Mart suppliers, but do not present formal hypotheses since our expectations are less strong. In order to test these hypotheses, we use financial performance as an indicator of the relative power a manufacturer holds versus a retailer. This approach is consistent with the work of Farris and others. It would be hard to conclude that Wal-Mart has not grown more powerful than its suppliers if support were found for H1a. Rather than being squeezed by the more powerful firm, however, Wal-Mart suppliers may incur lower performance deliberately in order to take advantage of benefits from working with this large retailer. For examining this issue, future studies may wish to employ survey research to obtain insight as to the strategic purpose of discounts offered.
4. Method Our analysis utilizes Compustat data on publicly owned firms by primary four-digit SIC code, combining the years 1988 to 1994. Although the data span several years, they are cross-sectional rather than a time-series because the same firms are not represented in each year. The names of firms within an industry change from year to year, perhaps reflecting bankruptcies, mergers, or other changes in ownership. Descriptions of the variables, industries, and firms studied are included in Appendixes I through III. Note that the sales, profits, and other dollar amounts from the Compustat database were converted to constant 1984 dollars using the producer price index to adjust for inflation. One key piece of information that we utilize (as did Kalwani and Narayandas 1995) includes the respondent-firm entries of the names of up to four firms they identified as “Primary Customers” stored in the Compustat database. These are customers who accounted for at least 10% of the firm’s annual sales revenue. Only industries in which at least one firm reported Wal-Mart as a primary customer in the year 1994, the last year in our data base, were included. In other words, in order for a firm to qualify for our sample, either Wal-Mart was one of its primary customers, or one of its competitors’ primary customers, during the year 1994. The sample was restricted in this way so that the financial performance of Wal-Mart suppliers would only be compared to the performance of non-Wal-Mart suppliers from similar industries (i.e., four-digit SIC codes). If Wal-Mart was not a primary customer to a supplier in an industry in 1994, then the industry might be so different (e.g., not supplying retailers) that it would make an inappropriate basis for comparison. The data set has one observation for each firm in each reported sales year, resulting in a total of 6,676 firm-year observations. As indicated, firm names vary across the seven years. Four hundred and ninety-eight firms provided data for all the years, but most of these were not Wal-Mart suppliers. We defined a manufacturer to be a Wal-Mart supplier if Wal-Mart appeared anywhere in the primary customer fields. Thus, some of our Wal-Mart suppliers only had Wal-Mart as a primary customer and others had Wal-Mart plus one to three other firms as primary customers. We also identified a group of other manufacturers to be Other Suppliers, meaning that they did not list Wal-Mart as a primary customer, but did list at least one other retail firm as a primary customer. Among the customers listed were Home Shopping Network, K-Mart,
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Radio Shack, Sears, Toys R Us, and the U.S. Department of Defense, as well as other companies that were less obviously involved in retailing. Finally, those manufacturers that did not list any primary customers were defined as Unaffiliated Suppliers. Of course, it is important to note that the primary customer field is optional in the Compustat data. Although we have assumed that a blank in this field indicates that a firm does not have major customers or is unaffiliated, a blank could also mean the firm simply failed to report the information. To the extent that this nonreporting bias exists in the database, it should lead to more similarity between firms we defined as Wal-Mart suppliers and those we defined as Unaffiliated Suppliers. Hence, any nonreporting bias should tend to reduce the magnitude of effects detected from a Wal-Mart connection. To test the hypotheses, the relationship between market share and financial performance was examined for each of the three types of suppliers. Financial performance, ZPROFIT, represents the z-score for net profits obtained by calculating how many standard deviations a given supplier’s net profits was above or below the mean net profits earned in an industry. By using this measure, one can make the analysis across many industries and begin to control for the variation in a supplier’s net profits that might be explained by the uniqueness of markets. We tested the following regression models to provide statistical evidence about the hypotheses: ZPROFIT ⫽ a ⫹ 1WM1 ⫹ 2MKTSHR ⫹ 3WM1*MKTSHR ⫹ 4LNETSALES ⫹
(1)
where: WM1 ⫽ 1 if Wal-Mart is a primary customer, 0 if no primary customers MKTSHR ⫽ ratio of firm’s annual net sales to total industry net sales WM1*MKTSHR ⫽ interaction of Wal-Mart dummy variable and market share LNETSALES ⫽ log of firm net sales ZPROFIT ⫽ a ⫹ 1WM2 ⫹ 2MKTSHR ⫹ 3WM2*MKTSHR ⫹ 4LNETSALES ⫹
(2)
where: WM2 ⫽ 1 when Wal-Mart is a primary customer, 0 if another retailer was a primary customer ZPROFIT ⫽ a ⫹ 1OTHER ⫹ 2MKTSHR ⫹ 3OTHER*MKTSHR ⫹ 4LNETSALES ⫹
(3)
where: OTHER ⫽ 1 if a firm other than Wal-Mart is the primary customer, 0 if no primary customers We employed the first model on a subsample of suppliers that had either identified Wal-Mart or no retailers as a primary customer. The second model was used on a subsample of suppliers that either had identified Wal-Mart as a primary customer or had identified another retailer as a primary customer. The third model was used on suppliers that either had
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another retailer besides Wal-Mart as a primary customer or had identified no retailer as a primary customer. The coefficient on WM1 in model 1 determines whether having Wal-Mart as a primary customer hurts or helps a supplier’s profitability as compared to unaffiliated suppliers (i.e., test H1a(1) versus H1b(1)). Similarly, we use the coefficient on WM2 in model 2 to determine whether having Wal-Mart as a major customer hurts or helps a company’s profitability compared to suppliers of other companies (i.e., test H1a(2) versus H1b(2)). We use the coefficient for Other in model 3 to determine whether supplier profits are hurt or helped when they have major commitments to such retailers as compared to when they do not (unaffiliated). In all models, the log of net sales was used to control for scale. Similarly, supplier market share was employed to control for upstream market power. We control for market share in all three models by including this variable plus an interaction term using dummy variables. The market share interaction in models 1 and 2 determines whether the Wal-Mart relationship boosts the profit they obtain from market share. As noted earlier, if the working relationship with Wal-Mart provides the supplier with greater efficiencies, such suppliers should enjoy greater profits from higher market share than others not so affiliated (i.e., testing H2(1) and H2(2)). In order to control for variation in economic conditions across years, a dummy variable for each sales year was included in a first regression model (YR88-YR94). The year 1994 served as the omitted category. A similar approach has been used frequently in econometrics studies (Neter et al.,1990, p.375). However, the results from this approach were not substantively different from those achieved with the simpler models and are not reported.
5. Results The results of the regressions are reported in Table 1. Not surprisingly, they reflect higher supplier profits from both greater category market share and from greater scale of operation. This pattern appears strongly across all three of the models. The coefficients on both WM1 and WM2 are significant and negative. This indicates that when Wal-Mart is a primary customer that profits fall for suppliers regardless of size. Wal-Mart, in other words, takes a bite in exchange for being a major customer for the supplier. This supports H1a, as opposed to H1b, indicating that Wal-Mart suppliers are squeezed financially more than their counterparts, ceteris paribus . However, at the same time, the positive coefficient on the interaction terms in the first two models suggests that Wal-Mart suppliers obtain greater profit from their market share than do their counterparts. This is commensurate with both parts of H2. While the data do not permit the calculation of the trade-off between joining the Wal-Mart team, this second finding suggests that where Wal-Mart suppliers obtain larger market shares, profitability is greater than for their counterparts. The results for the third model indicate a difference between supplier relationships with other retailers as compared to Wal-Mart. Here, we find that smaller retailers do not take the bite that Wal-Mart does, as the dummy coefficient for Other was not significant. We also see,
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Table 1 Regression Results Z-Profit is Dependent Variable Ind. Vars.\Sample
WM vs. No-Primary
WM vs. Other Primary
Other Primary vs. No Primary
Intercept MarketShare Log(Netsales) WM1 WM1*MktShare WM2 WM2*MktShare Other Primary Rtlr. Other*MktShare Rtlr. R2 Adj. R2 Sample Size
⫺0.78*** 1.19*** 0.16*** ⫺0.24*** 0.46*
⫺0.66*** 1.12*** 0.12***
⫺0.72*** 1.25*** 0.15***
0.35 0.35 3,962
⫺0.20*** 0.66*** 0.33 0.33 2,931
⫺0.02 ⫺0.25*** 0.34 0.34 6,598
*** Significant at 0.01 level ** Significant at 0.05 level * Significant at 0.10 level Sub-Samples & Variable Definitions: Sub-sample includes suppliers of Wal-Mart and retailers not identified as primary customers. WM1 ⫽ Wal-Mart dummy variable ⫽ 1 Sub-sample includes only suppliers reporting a primary retailer, including Wal-Mart and all others so designated. WM2 ⫽ Wal-Mart dummy variable ⫽ 1; Sub-sample includes suppliers with other primary retail customers and those with no primary retail customers. Other ⫽ other primary retailer dummy variable ⫽ 1.
and surprisingly, that efficiencies in supplier operations are not derived from major relationships with these other retailers. The Other interaction coefficient is significantly negative. This indicates that as suppliers to Other retailers increase their market share, they actually lose profitability more rapidly than is the case for unaffiliated suppliers. 5.1. Significance We ran several tests to determine whether the differences between the dummy variable coefficients (Wal-Mart, Other, Unaffiliated) were significant. In other words, we tested for differences in the intercepts due to a firm being a supplier of Wal-Mart, another primary customer or because the firm has no primary customers. We ran a regression model including the entire sample (all three supplier types), and the set of independent variables, including interactions, used in the three models described above. Using these estimates, we used the F-test to determine whether the coefficient on the Wal-Mart dummy variable was equal to the coefficient on the Other dummy variable. The results of this test suggest that these coefficients are significantly different from each other. We made similar F tests to determine whether the coefficient on Wal-Mart was significantly different from the coefficient on the intercept, which in this case represents the omitted, unaffiliated supplier category. Again, the test suggests that these coefficients are significantly different. We ran additional coefficient tests for significant differences between Other and
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Table 2 Pearson Correlation Coefficients Prob ⬎ [R] under 0 N ⫽ 6,676 WALMART
WALMART 1.000 Prob. 0.000 OTHER 0.079 Prob. 0.000 ZPROF ⫺0.003 Prob. 0.831 MKTSHR 0.041 Prob. 0.001 WALMART*MKTSHR 0.550 Prob. 0.000 OTHER*MKTSHR 0.093 Prob. 0.000 LOG OF NETSALES 0.033 Prob. 0.006
OTHER Z-PROFIT MKTSHR WALMART* OTHER* LOG OF MKTSHR MKTSHR NET SALES 0.079 ⫺0.003 0.000 0.831 1.000 ⫺0.100 0.000 0.000 ⫺0.100 1.000 0.000 0.000 ⫺0.059 0.440 0.000 0.000 0.055 0.086 0.000 0.000 0.359 0.192 0.000 0.000 ⫺0.138 0.526 0.000 0.000
0.041 0.001 ⫺0.059 0.000 0.440 0.000 1.000 0.000 0.194 0.000 0.546 0.000 0.396 0.000
0.550 0.000 0.055 0.000 0.086 0.000 0.194 0.000 1.000 0.000 0.260 0.000 0.066 0.000
0.093 0.000 0.359 0.000 0.192 0.000 0.546 0.000 0.260 0.000 1.000 0.000 0.179 0.000
0.033 0.008 ⫺0.138 0.000 0.526 0.000 0.396 0.000 0.066 0.000 0.179 0.000 1.000 0.000
the Intercept (the omitted Unaffiliated category), and for significant differences between the interaction terms (Wal-Mart*Mktshr and Other*Mktshr). In each case, the F tests indicate that these pairs of variables are significantly different from each other. Thus, we conclude that Wal-Mart’s effect on supplier profits is significantly different than the effects due to dealing with other major retailers. In addition, having higher market share combined with having a relationship with Wal-Mart has a significantly different effect on supplier profits than having a relationship with another retailer while having high market share. 5.2. Multicollinearity Table 2 presents the correlation matrix for all variables in the pooled sample of Wal-Mart suppliers, Other Suppliers, and Unaffiliated Suppliers. In order to check for multicollinearity between independent variables in our regression models, we computed variance inflation factors. These statistics show how multicollinearity causes instability in the coefficient estimates. Variance inflation factors higher than 10 are commonly considered to be cause for concern. In these three regression models, all variance inflation factors were less than 2.0. Another way to assess multicollinearity is to examine the eigenvalues of the correlation matrix of the set of independent variables in each model. Very small eigenvalues, or large variability among eigenvalues, suggests that multicollinearity may be a problem. The condition number, or the square root of the ratio of the largest to smallest eigenvalue, reflects this variability. Values greater than 30 are considered to be indicative of multicollinearity. Out of our three models, the highest condition number was 4.96. Based on these results, we conclude that multicollinearity is not affecting the results of our regression analyses.
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Table 3 Descriptive Statistics on Size and Profit Measures by Supplier Type Wal-Mart Suppliers
Net Sales* Log of Net Sales Net Profit* Net Profit Z-Score Market Share *
Other Suppliers
Mean
Std Dev
Med
Mean
Std Dev
366.93 4.70
583.87 1.68
104.91 4.65
419.79 3.59
1643.02 2.34
42.14 0.12
77.98 1.01
6.69 ⫺0.33
34.12 ⫺0.11
125.09 0.77
0.14
0.20 N ⫽ 78
0.04
0.10
0.20 N ⫽ 2853
Unaffiliated Suppliers Med
Mean
Std Dev
Med
33.91 3.52
884.15 4.31
2595.64 2.76
101.41 4.62
1.93 ⫺0.34
95.24 0.08
280.89 1.04
7.08 ⫺0.31
0.01
0.13
0.23 N ⫽ 3745
0.02
in millions of dollars
6. Discussion Wal-Mart suppliers in our database are relatively smaller than their counterparts in terms of sales. Table 3 provides descriptive statistics about the Wal-Mart, other, and Unaffiliated Suppliers. As smaller entities, they earn lower profits than larger entities and it may be possible to attribute this poorer performance to their connection to Wal-Mart. The regression results indicate that the presence of Wal-Mart as a primary customer hurts the financial performance of a supplier, especially when the supplier has a small market share. This supports Plotkin’s position (Plotkin, 1997) that small suppliers do not fare well with Wal-Mart. However, the data also indicate that a Wal-Mart relationship may help a supplier when it obtains a large market share, especially if this is accomplished with Wal-Mart’s help. It is nevertheless instructive to compare Wal-Mart supplier performance against suppliers with major relationships with other retailers. Here, we find that despite its bargaining strength, Wal-Mart may offer a better relationship than its smaller retail competitors. The market share and the interaction market share coefficients are superior for the comparison of Wal-Mart suppliers to unaffiliated suppliers as compared to the coefficients for the comparison of suppliers to other retailers to unaffiliated suppliers. Conceivably these weaker (negatively-signed) results for suppliers with major commitments to Other retailers could be explained by the failure of the unaffiliated suppliers to complete the item on the Compustat questionnaire when they actually were Wal-Mart suppliers. The better-performing, larger-share, unaffiliated suppliers could actually include many Wal-Mart suppliers if this were the case. However, it may also be the case that Wal-Mart simply does a better job of forging relationships that permit its suppliers to be more efficient when they get large. Or, larger-share suppliers that distribute primarily through retailers other than Wal-Mart may be at a disadvantage and perform poorly when forced to compete with larger-share rivals that have strong Wal-Mart connections. Unfortunately, our data did not allow us to track more than a few individual firms over time to see if they improved their market shares or efficiency while selling to Wal-Mart. Such
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a tracking study would help determine whether the impact of the dynamics of a Wal-Mart relationship upon a supplier’s profits. Another direction for such tracking studies that could be helpful to public policy makers would be to determine whether suppliers to Wal-Mart are able to leverage their relationship to increase their market shares. Their exposure with Wal-Mart may provide improved recognition of their brand. It may also permit them to exclude competitors from shelf space and to extend market share. As discussed earlier, the antitrust enforcement agencies have become interested in whether agreements between suppliers and retailers lead to exclusion of competing suppliers.
7. Conclusion Our results show that it is not possible to identify the impact of Wal-Mart upon supplier profits unambiguously. Ceteris paribus, we find that suppliers that identify Wal-Mart as a primary customer perform more poorly financially than those that do not identify themselves in this way. But, these results do not suggest that suppliers “Should you just say no to Wal-Mart?” While Wal-Mart may be using its power to squeeze suppliers, it is also possible that suppliers are willing to make concessions in the hope that a Wal-Mart relationship will help them expand their market share. Further, our data suggest that developing a relationship with Wal-Mart could lead to a boon rather than a bust for the astute manufacturer, once a certain market share is reached. Large-share suppliers to Wal-Mart extract more profits from their market share than do their counterparts without such a relationship. This raises another question of interest. Could Wal-Mart leave this money on the table deliberately in order to attract better suppliers?
Acknowledgement The views expressed in this paper do not represent the views of Freddie Mac, its management or shareholders. The authors are deeply appreciative of the comments provided by Charlotte Mason, the Journal of Retailing reviewers, and the editor, L. P. Bucklin.
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Appendix I - List of Compustat Standard Industry Classification (SICs) that include Wal-Mart Supplier Firms SIC Code
Name
SIC Code
Name
2253
APPAREL
2387
2899 2066
CHEMICAL PRODUCTS CONFECTIONERY
3944 2834
2731 3634 2879 3663 3469
CONSUMER PRODUCTS CONSUMER PRODUCTS & SERVICES DISINFECTANTS ELECTR COMMUNICATIONS SYS FABRICATED METAL PRODUCTS
2369 3842 2325 2321 3911
2676
FEMININE HYGIENE & CONTRCPTIVES
3751
3861
FILM PROCESSORS
3579
3089
FLOORING PRODUCTS
3827
3142
FOOTWEAR
2782
2844 2329
FRAGRANCES & COSMETICS GOLF APPAREL
2392 2679
2771
3524
3171
GREETING CARDS & RELATED PRODUCTS HANDBAGS & APPAREL ITEMS
3635
HOME CLEANING SYSTEMS
2393
2842 3365 3080
HOUSEHOLD GROCERY PRODUCTS HOUSEWARES INFANT & CHILD PRODUCTS
3651 3949 3661
2399 3812 3961 3645
INFANT PRODUCTS INSTRUMENT PRODUCTS JEWELRY LAMPS
2211 3942 2339 2326
LEATHER GOODS & ACCESSORIES LEISURE PRODUCTS MAMMALIAN CELL CULTURE SYS MANUFACTURING MECHANICAL HEART VALVE MENS APPAREL MENS SPORTSWEAR MOTIVATION-RECOGNITION PRODUCTS MOTORCYCLES & RELATED PRODUCTS OFFICE & GRAPHIC ARTS PRODUCTS OPTICAL COMPONENTS & SYSTEMS PERSONAL ORGANIZERSPLANNERS PILLOWS & BEDDING PRODUCT IDENTIFICATION SYSTEMS PROFESSIONAL-GARDENLEISURE PRODUCTS ROOFTOP AIR-CONDITIONING & HEATING EQUIPMENT SMOCK & CLOTH BAG MANUFACTURING SPEAKER SYSTEMS SPORTS ACCESSORIES TELECOMM ACCESS & TEST PRODUCTS TEXTILE MILL PRODUCTS TOYS WOMENS APPAREL WORK APPAREL
3585
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393
Appendix II - Top 25 Wal-Mart Suppliers in the Compustat Database*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Company Name
Net Sales in millions of dollars
HERSHEY FOODS CORP POLAROID CORP NEWELL RUBBERMAID INC CLOROX COMPANY SPRINGS INDUSTRIES SUNBEAM CORPORATION RUSSELL CORP PARAGON TRADE BRANDS INC SMITH CORONA CORP EKCO GROUP INC OROAMERICA INC MCKESSON HBOC INC CROWN CRAFTS INC WINDMERE-DURABLE HOLDINGS DEL LABORATORIES INC NATIONAL PRESTO INDUSTRIES INC TANDY BRANDS ACCESSORIES INC SCOTT’S LIQUID GOLD NOEL GROUP INC DEL LABORATORIES INC PREMIUMWEAR INC HOME PRODUCTS INT’L INC DAKOTAH INC L A T SPORTSWEAR INC AJAY SPORTS INC
2987.80 1915.91 1719.08 1529.25 1209.69 992.88 909.91 479.39 230.85 221.25 181.79 179.62 174.78 150.05 117.39 106.11 56.71 44.25 43.06 40.11 34.74 29.66 25.19 19.98 10.69
* Based on 1994 annual net sales volume.
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Appendix III - Top 25 Suppliers to Non-Wal-Mart (Other) Primary Customers in the Compustat Database*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Company Name
Net Sales in millions of dollars
NEC CORP CANON INC NORTEL NETWORKS CORP BCE INC WHIRLPOOL CORP THOMSON CSF GENERAL MOTORS GENERAL MOTORS CL H ROCKWELL INTL CORP NIKE INC ALLIED SIGNAL INC RAYTHEON CO LOCKHEED MARTIN CORP REEBOK INTERNATIONAL LTD ALLIED SIGNAL INC UNITED TECHNOLOGIES CORP MATTEL INC LEAR CORP FORT JAMES CORP ROCKWELL INTL CORP HASBRO INC CBS CORP YORK INTL EASTMAN CHEMICAL CO TENNECO INC
35895.53 13579.70 7368.68 7167.57 6585.75 5640.59 4614.00 4321.87 4154.93 3944.35 3830.16 3370.34 3359.57 2717.83 2710.85 2666.11 2655.36 2607.71 2529.88 2341.34 2212.31 2043.91 2006.52 1958.57 1809.44
* Based on 1994 annual net sales volume.
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References Ailawadi, K. L. (2001). The Retail Power-Performance Conundrum: What Have We Learned? Journal of Retailing, forthcoming. Ailawadi, K. L., Borin, N., & Farris, P. W. (1995). Market Power and Performance: A Cross-Industry Analysis of Manufacturers and Retailers,“ Journal of Retailing, 71(3), 211–248. Anderson, E., Lodish, L. M., & Weitz, B. A. (1987). Resource Allocation Behavior in Conventional Channels,“ Journal of Marketing Research, 24 (February), 85–97. Anderson, J. C., & Narus, J. A. (1991). Partnering as a Focused Market Strategy, California Management Review, 33(Spring), 95–113. Bloom, P. N., Gundlach, G. T., & Cannon, J. P. (1998). Slotting Allowances and Fees: Schools of Thought and the Views of Practicing Managers. Journal of Marketing, 64(April): 92–108. Bowman, R. J. (1997). Should You Just Say No to Wal-Mart? Distribution, 96(12), 52–54. Buzzell, R. D., & Gale, B. T. (1987). The PIMS Principles: Linking Strategy to Performance. New York: The Free Press. Buzzell, R. D., Quelch, J. A., & Salmon, W. J. (1990). The Costly Bargain of Trade Promotion. Harvard Business Review, 68 (March-April), 141–149. Cadotte, E. R., & Stern, L. W. (1979). A Process Model of Interorganizational Relations in Marketing Channels. In Jagdesh Sheth (Ed.), Research in Marketing: Volume 2, (pp. 127–158). Greenwich, CT: JAI Press. Desai, P. S. (1998). Multiple Messages to Retain Retailers: Signaling New Product Demand, working paper, Fuqua School of Business, Duke University. El-Ansary, A. I., & Stern, L. W. (1972). Power Measurement in the Distribution Channel. Journal of Marketing Research, 9(1), 47–52. Elman, D., & Hughes, B. (1988). How Much are Deals Driving BM/HBA? Supermarket Business, (April), 57– 60. Erdem, S. A., & Harrison-Walker, L. J. (1997). Managing Channel Relationships: Toward an Identification of Effective Promotional Strategies in Vertical Marketing Systems, Journal of Marketing Theory and Practice, 5 (Spring), 80 – 87. Etgar, M. (1976). Channel Domination and Countervailing Power in Distributive Channels. Journal of Marketing Research, 13(3), 254 –262. Farris, P. W., & Ailawadi, K. L. (1992). Retail Power: Monster or Mouse? Journal of Retailing, 68(4), 351–369. Frazier, G. L. (1983). Interorganizational Exchange Behavior in Marketing Channels: A Broadened Perspective. Journal of Marketing, 47(4), 68 –78. Frazier, G. L., & Lassar, W. M. (1996). Determinants of Distribution Intensity. Journal of Marketing, 60(4), 39 –51. Federal Trade Commission (1998). FTC Upholds Charges the Toys ‘R’ Us Induced Toy Makers to Stop Selling Desirable Toys to Warehouse Clubs, FTC News Release, October 14. Gaski, J. F. (1984). The Theory of Power and Conflict in Channels of Distribution. Journal of Marketing, 48(3), 9 –29. Hunt, S. D., & Nevin, J. R. (1974). Power in a Channel of Distribution: Sources and Consequences. Journal of Marketing Research, 11(2), 186 –193. Johnson, W. C. (1988). Sales Promotion: It’s Come Down to “Push Marketing”. Marketing News (February 29): 8. Kalwani, M. U., & Narayandas, N. (1995). Long-Term Manufacturer-Supplier Relationships: Do They Pay Off for Supplier Firms? Journal of Marketing, 59 (January): 1–16. Kim, S. Y., & Staelin, R. (1998). “Retail Power: Is it an Illusion?” working paper, Fuqua School of Business, Duke University. Lariviere, M. A., & Padmanabhan, V. (1997). Slotting Allowances and New Product Introductions. Marketing Science, 16(2), 112–128. Lusch, R. F. (1976). Sources of Power: Their Impact on Intrachannel Conflict. Journal of Marketing Research, 13(4), 382–390.
396
P.N. Bloom, V.G. Perry / Journal of Retailing 77 (2001) 379 –396
Messinger, P. R., & Narasimhan, C. (1995). Has Power Shifted in the Grocery Channel? Marketing Science, 14, 189 –223. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied Linear Statistical Models: 3rd Edition. Burr Ridge, IL: Richard D. Irwin. Plotkin, H. (1997). Wal-Mart Throws the Book at Small-Biz Vendors. Inc. Magazine, (January). Schifrin, M. (1996). The Big Squeeze. Forbes, March 11, 1996, 45– 46. Stern, L. W., ed. (1969). Distribution Channels: Behavioral Dimensions. Boston: Houghton Mifflin. Stern, L. W., El-Ansary, A. I. (1977). Marketing Channels. Englewood Cliffs, NJ: Prentice-Hall. Stern, L. W., & Eovaldi, T. L. (1984). Legal Aspects of Marketing Strategy: Antitrust and Consumer Protection Issues. Englewood Cliffs, NJ: Prentice-Hall. Stern, L. W., & Reve, T. (1980), Distribution Channels as Political Economies: A Framework for Comparative Analysis. Journal of Marketing, 44(Summer), 52– 64. Sullivan, M. W. (1997). Slotting Allowances and the Market for New Products. Journal of Law and Economics, 40(October), 461– 493. Weir, T. (2000). Slotting in the Spotlight. Supermarket Business, 55(7), 1–14.