Consumer Reactions to Price-Matching Signals

Subimal Chatterjee, SUNY at Stony Brook
Suman Basu Roy, Rutgers University
ABSTRACT - Price matching or the practice whereby sellers guarantee to match the lowest market price can either raise market prices through price collusion or lower market prices through more competition. Sixty-five undergraduate business students and 41 MBA students served as subjects in an experiment that tested these conflicting theories. Prior to participating in the experiment, the MBA students analyzed a business case that described how price matching by two industrial sellers led to implicit price collusion over a period of time and higher market prices. Yet, when given a choice between stores that advertised to match each other’s prices and stores that did not, both MBAs and undergraduates preferred the former and expected that they would get lower prices there. However, when MBA subjects assumed the role of a price-setting seller whose only competitor had announced a pric-matching policy, they appeared to understand the collusive possibilities of such arrangements. Implications of such buying-selling asymmetry are discussed.
[ to cite ]:
Subimal Chatterjee and Suman Basu Roy (1997) ,"Consumer Reactions to Price-Matching Signals", in NA - Advances in Consumer Research Volume 24, eds. Merrie Brucks and Deborah J. MacInnis, Provo, UT : Association for Consumer Research, Pages: 400-404.

Advances in Consumer Research Volume 24, 1997      Pages 400-404

CONSUMER REACTIONS TO PRICE-MATCHING SIGNALS

Subimal Chatterjee, SUNY at Stony Brook

Suman Basu Roy, Rutgers University

ABSTRACT -

Price matching or the practice whereby sellers guarantee to match the lowest market price can either raise market prices through price collusion or lower market prices through more competition. Sixty-five undergraduate business students and 41 MBA students served as subjects in an experiment that tested these conflicting theories. Prior to participating in the experiment, the MBA students analyzed a business case that described how price matching by two industrial sellers led to implicit price collusion over a period of time and higher market prices. Yet, when given a choice between stores that advertised to match each other’s prices and stores that did not, both MBAs and undergraduates preferred the former and expected that they would get lower prices there. However, when MBA subjects assumed the role of a price-setting seller whose only competitor had announced a pric-matching policy, they appeared to understand the collusive possibilities of such arrangements. Implications of such buying-selling asymmetry are discussed.

Firms often publicly commit themselves to match or beat competitors’ prices in industrial as well as consumer markets. In industrial contracts, for example, meeting competition clauses guarantee customers that firms will meet prices offered by competitors. Similarly, most favored customer clauses give customers the right to obtain the lowest price paid by any other customer (see Levy, 1994). In consumer markets, retailers often pledge to match or beat competitors’ current prices as shown by the following newspaper insert from Sears Roebuck (Chicago Tribune, March 5, 1989)

"Yes, we’ll meet or beat the competition’s current advertised price on the identical item! Just bring the competition’s current ad to any of our retail stores"

and Montgomery Ward’s retaliatory announcement on the same day

"We’ll match any competitor’s current advertised sale price on national brand names 365 days a year! If you see a lower advertised price for the same national brand name item, we’ll match that price at the time of purchase or i f you find a lower advertised price within 30 days after purchase, just bring in your receipt and we’ll gladly refund the difference" (as quoted in Coughlan and Vilcassim, 1989: ps. 19-20).

In fact, supermarkets carry this policy one step further by offering to honor store coupons offered by other supermarkets, thereby effectively matching "coupon prices" (Hess and Gerstner, 1991).

Although price matching has been extensively researched in the industrial economics literature, little attention has been devoted to studying how consumers interpret and respond to such signals. Understanding consumer responses to such signals is important since price matching can facilitate tacit collusion among sellers and result in higher market prices (e.g., Zhang, 1995). Hence, if consumers think that price matching signals lower market prices, then proper decision aids have to be designed to help consumers correctly interpret such signals and make the proper shopping choice. We report an exploratory experiment that investigates how undergraduate and MBA subjects react to price-matching signals. Prior to participating in the experiment, the MBA subjects analyzed a real-world business case that demonstrated how price matching by two industrial sellers led to tacit price collusion and higher market prices over a period of time. The undergraduates received no such instructions. The experiment we report examines the differences in the reactions of the MBAs and undergraduates to price-matching announcements when they pay the role of price-taking consumers as well as price-setting sellers.

THEORY

Several theories have been advanced as to how sellers may use price matching as a strategic tool, and how consumers may use these signals in their search and purchase decisions (see Levy, 1994; Hess and Gerstner, 1991 for reviews). At the simplest level, stores desiring to build market share at the expense of profits may use price matching to convince customers that they will find low prices if they visit the store. Consumers, on the other hand, may be encouraged to search more for the lowest price, thereby lowering market prices. If consumers have to bring proof of competitors’ lower advertised prices, then price matching may discriminate between the more knowledgeable and less knowledgeable consumers (e.g., Png and Hirshleifer, 1987). Less knowledgeable consumers, unable or unwilling to search for prices at other stores, may be charged higher prices than their more knowledgeable counterparts.

Sellers may benefit strategically by offering to match prices. For example, if all sellers maintain high prices, and one seller is inclined to reduce prices to gain market share, all others may follow suit, thereby reducing industry profits. Price-matching arrangements, therefore, may serve as deterrents to price cuts, since the seller contemplating the price cut knows that other competitors are likely to retaliate by initiating their own price cuts. Taking this argument one step further, some authors have suggested that price-matching can support a collusive price, encouraging sellers to raise prices to levels that maximize joint profits (Salop, 1986; Sargent 1993; Baye and Kovenock, 1994; Chen, 1995; Zhang, 1995). The little empirical data that exists support this notion. Hess and Gerstner (1991) collected weekly price data for 114 frequently purchased products from five supermarkets in North Carolina between 1984 and 1986. Out of these 114 products, 79 were covered under price-matching guarantees and their prices published in a weekly Price Finder. The other 35 products that were not included in the Price Finder (due to fluctuations of wholesale prices or legal issues) were not guaranteed to match the lowest market price. The authors found that not only did price matching result in a high degree of price coordination between the supermarkets engaging in the price-matching schemes, but also resulted in greater price coordination among all supermarkets, with or without price-matching policies. Over time, there was a significant increase in prices of the products covered by the price-matching policy relative to the products that were not.

Unlike research on sellers’ reactions to price-matching signals, not much research exists on consumers’ reactions to such signals. The marketing literature, for example, documents how consumers are affected by unilateral price promotions such as "x% off" or "x dollars off" regular prices. Unilateral signals, however, are different from competitive signals such as price matching. The latter do not advertise the amount of price cut, but only promise to match a lower price should the customer find one. In this case, the customer does not know whether the current store-price of an item is any different from the original price, i.e., if s/he is getting a deal, and, lowest in the market. The absence of any information about the amount of price reduction and/or competitor prices may make consumers suspicious, more vigilant, and encourage them to search for the lwest available price. Conversely, consumers may (wrongly) assume that stores guaranteeing to match the lowest market price, must have the lowest prices themselves, and hence, use the price-matching signal as a heuristic to simplify the decision of making a store choice (e.g., "choose any store that advertises to match the lowest market price.")

TABLE 1

EXPERIMENTAL STIMULI

Since price-matching practices can lead to higher market prices, the issue becomes how to make consumers aware of this possibility so that they can make informed decisions. The current experiment attempts to accomplish this in two ways. First, we put subjects in the role of sellers whose task is to set prices in a market where all sellers engage in price matching. Putting subjects in the role of sellers may make them more aware about the collusive potentials of price-matching practices, a notion that has to be tested empirically. Second, and more directly, we make some subjects analyze a real-world business case that explicitly deals with the collusive aspects of price-matching policies. We expect that when subjects are given actual market data about implicit price collusion resulting from price-matching practices, they will be less likely to choose stores making such claims.

The experiment that follows puts 106 subjects in the role of price-taking buyers and price-setting sellers and investigates their responses to price-matching signals. Sixty-five subjects (undergraduate business students) approached the experiment without any formal instructions on price-matching strategies and their consequences, while the remaining 41 subjects (MBAs) were instructed about the anti-competitive aspects of such strategies through the analysis of a business case.

EXPERIMENT

Method

Stimuli and measures. Subjects were asked to imagine that they planned to buy a couch, and had to decide which of the following two townships, X or Y to visit. Both townships were equidistant from where subjects lived, and subjects were told that they had time to visit only one township. Each township had two furniture stores as described in Table 1. Following the descriptions, subjects were given a choice between Township X, Township Y, or remain indifferent. Thereafter, they were asked (1) about their impressions of prices in the two Townships, on a 9 point scale ranging from 1 (Definitely Lower in Township X) to 9 (Definitely Higher in Township X) with 5 (About the Same) as the mid- point, and (2) the perceived difficulty of the decision, on a 9 point scale ranging from 1 (Not at all Difficult) to 9 (Very Difficult). On the next page of the stimuli booklet subjects were asked to imagine that they were about to open a new furniture store. Their only competitor had recently announced that it would match the lowest advertised price on comparable items. Subjects indicated whether they, too, would offer a similar price-matching guarantee (Yes / No) or remain indifferent. Once the subjects had made their choice, they were told that the Beter Business Bureau had prepared a list of suggested prices for furniture retailers. The list was not available to consumers. Subjects rated the price they would set on a 1 to 9 scale, ranging from 1 (Definitely Lower than the Bureau’s Suggested Prices) to 9 (Definitely Higher than the Bureau’s Suggested Prices), with 5 (About the Same) as the mid-point. The order in which subjects played the role of buyer and seller (buyer first, then seller vs. seller first, then buyer) was counterbalanced across subjects.

Subjects. Sixty-five undergraduate business students enrolled in a business strategy class at a large northeastern university, and 41 MBA students enrolled in a business strategy class at another large northeastern university served as subjects. The undergraduate business students had little or no work experience. For these subjects, the experiment was run during the first week of regular classes, when little material had been covered in class. To the best of our knowledge, these subjects had never been exposed to case or text materials on price-matching. The MBA students, on the other hand, had substantial work experience. For these subjects, the experiment was conducted towards the latter half of the semester. Four weeks prior to the experiment, as part of their course requirements, these students were given to analyze a real world business case dealing with the pricing policies of General Electric and Westinghouse for turbine generators during the mid 1960’s (General Electric Vs. Westinghouse in Large Turbine Generators (A), (B), (C), Harvard Business School, 1980, Case nos. 9-380-128, 129, 130). The case describes how General Electric instituted a price protection clause in their contracts that guaranteed customers that "in the event that prices were lowered, General Electric would retroactively reduce prices to any customers who had purchased a turbine generator in the previous six months" (General Electric vs. Westinghouse in Large Turbine Generators (B), Harvard Business School, 1980, 9-380-129). Westinghouse quickly followed with a similar price protection clause, and within months there was a rise in market prices. The Justice Department, after investigating the matter, concluded that the pricing system succeeded in assuring the two companies that the other would not deviate from the published prices, even though the companies did not directly or covertly communicate with each other. One full class period was devoted to the study of the case, at the end of which the students submitted a written report.

TABLE 2

STORE CHOICE AMONG MBAs AND UNDERGRADUATES

TABLE 3

PRICE PERCEPTION AMONG MBAs AND UNDERGRADUATES

TABLE 4

DECISION DIFFICULTY AMONG MBAs AND UNDERGRADUATES

Analyses and Results

There was no order effect of subject’s role (buyer then seller, seller then buyer) in any of the analyses. Hence the data are collapsed across the two conditions.

Store choice as buyers. Sixty four percent of the subjects chose Township X (where the stores advertised price-matching policies) while the remainder chose Township Y (where the stores did not offer to match prices), or were indifferent (Z=2.91, p<0.005). The pattern was uniform across MBAs and undergraduates. Even the MBA students who were made aware about the possibilities of price collusion in price-matching schemes preferred Township X to Township Y (see Table 2).

Price expected as buyers. It is reasonable to assume that subjects choosing to visit Township X would expect lower prices at the stores in Township X, whereas subjects chosing Township Y would expect lower prices at the stores in Township Y. Indeed, when subjects’ price expectations, ranging from 1 (Definitely Lower in Township X) to 9 (Definitely Higher in Township X), with 5 (About the Same) as the mid point, were subjected to a two-way ANOVA (SAS GLM) with choice of Township (Township X, Township Y, Indifferent) and population (MBAs vs. Undergraduates) serving as predictors, only the main effect of choice was significant (F(2,100)=30.00, p<0.001; MX=3.62, MIndifferent=4.74, and MY=6.11). We conducted follow-up tests that examined the "strength" of their expectations, i.e., how firmly were subjects convinced that they would get lower prices in the township they chose to visit. To do this we transformed subjects ratings around the midpoint of the scale into a variable "Delta" defined as follows:

Delta = 5 - Subject’s Rating, for subjects choosing Township X, and

= Subject’s Rating - 5, for subjects choosing Township Y.

Subjecting the variable Delta to a two way ANOVA with choice of township, and population as predictors however yielded no significant effects. Subjects choosing Township X and subjects choosing Township Y were equally convinced that they would get lower prices in Townships X and Y respectively (DX=1.38, DY=1.11; F(1,83)<1). The results were unchanged across MBAs and undergraduates (Interaction F(1,83)<1; see Table 3).

Felt decision difficulty as buyers. Consumers can use price-matching signals as a heuristic to simplify their choice of store (e.g., "visit the store that guarantees that it will match the lowest price"). We tested this hypothesis by subjecting decision difficulty ratings to a two-way ANOVA with choice of township and population serving as predictors. The main effect of choice was significant and showed that subjects choosing Township X felt their decision to be easier than those choosing Township Y or those who remained indifferent between the two (F(2,99)=5.31, p<0.01; MX=2.92, MY=4.16, MIndifferent=4.37). Contrasts using Dunnett’s test showed that, compared to the subjects who were indifferent between the two townships, subjects choosing Township X found the choice significantly easier (D=1.44, p<0.05), but not those subjects choosing Township Y (D=0.21). Once again the pattern was identical across MBAs and undergraduates (Interaction F(2,99)<0.84; see Table 4).

Choice as sellers. When subjects assumed the role of a seller and were asked what they would do if their only competitor announced a price-matching policy, 75% of them (79 out of 106) opted to retaliate with a similar clause (Z=5.04, p<0.001). The proportions were similar across MBAs and undergraduates (see Table 5).

TABLE 5

PRICE MATCHING POLICY ADOPTED BY MBAs AND UNDERGRADUATES

FIGURE 1

BUYING-SELLING ASYMMETRY IN RESPONSE TO PRICE-MATCHING SIGNALS

Comparing buying and selling prices. When subjects assumed the role of sellers, they were informed that te Better Business Bureau had prepared a list of suggested prices. They were asked how the price they would set compared with the Bureau’s prices on a 9 point scale, ranging from 1 (Definitely Lower than the Bureau’s Suggested Prices) to 9 (Definitely Higher than the Bureau’s Suggested Prices), with 5 (Same as the Bureau’s Prices) serving as the mid point. We compared this selling price that subjects set in response to a competitor’s price-matching claim to the buying price that subjects expected to find in stores that announced to match their competitors’ prices. One prediction is that the price set as a seller will parallel the price expected as a buyer. For example, as a consumer you may believe that price matching implies lower market prices through more competition. You may continue with this line of reasoning when asked to play the role of a seller and set lower prices compared to those who believe that price matching leads to price collusion and higher market prices. Another prediction is that, as a buyer, you may believe that price matching implies more competition and lower market prices, but as a seller you may realize the possibility of price collusion. Hence, the price you set may be just as high as those set by your counterparts who believe that price matching leads to collusion and higher market prices.

We tested these predictions by subjecting the selling price to an ANOVA with the price subjects expected as buyers and population as predictors. Subjects’ price expectations as buyers were mean centered to reduce colinearity and entered into the model as a continuous predictor. Only the interaction between population and price-expected was significant (F(1,101)=6.98, p<0.01). To explicate the interaction, a median split divided subjects into a "low" and "high" price group with respect to the price they expected as buyers. Among the MBAs, the "low" group (subjects who thought price matching led to lower prices) set higher prices than their counterparts who thought price matching led to higher prices (t(39)=2.14; p<0.05; MExpect Low Price=5.86, MExpect High Price=4.75; see Figure 1). Among undergraduates, however, there was no significant difference between the price set by the "low" and the "high" group (t(62)<1; MExpect Low Price=5.00, MExpect High Price=5.43).

The pattern of the means in Figure 1 shows that prices undergraduates expected to get as buyers had no influence on the prices they set as sellers. For the MBAs however, those who expected to get lower prices as buyers set significantly higher prices compared to those who expected to get higher prices as buyers. It appears that when they play the role of sellers, those MBA subjects who expected price-matching to lead to more competition and lower market prices, realized the collusive possibilities of such arrangements. The price they set as sellers were, in fact, higher than their counterparts who expected price matching to lead to higher prices. The implication is that informing customers about the possibilities of price collusion in price-matching schemes may not be enough. Rather, they should be encouraged to play the role of sellers if they are to recognize the collusive aspects of price-matching arrangements.

DISCUSSION

Price-matching promotions can send conflicting signals to consumers. On one hand, price matching may signal that sellers are colluding with each other to keep market prices high. On the other hand, price matching may signal stiff competition and lower market prices. In our experiment, subjects were more likely to associate price matching with lower rather than higher market prices. In fact, they may have used the signal as some sort of a decision heuristic to simplify their choice (e.g., "choose the store that advertises that it will match its competitor’s price"). Even MBA subjects who had read and analyzed a real life case dealing with price collusion in markets exhibited this bias. However, the latter subjects seemed to have realized the possibilities of collusion when they were put in the role of sellers. A direct implication is that consumers can avoid the trap of false signals and high prices if they asked themselves what they would do if they were the sellers.

Our experiment takes the first step toward understanding a very complex issue. Hence, we may have risked oversimplifying the environment when designing the stimuli. First, we examined price matching in the context of a durable product purchase, whereas empirical evidence is limited to frequently purchased grocery items. Since durables are purchased infrequently and typically entail high expenditure, consumers may rely on the price-matching signal to shorten search and simplify choice. Second, we gave no information other than the absence or presence of price-matching policies, setting up a rather artificial experimental environment. This may have forced subjects to focus on the signal, when in real life such signals may be superseded by other factors such as quality of the product, or image of the store. Third, our method of instructing subjects may not have been relevant to a consumer-goods buying context. Subjects analyzed a case that dealt with price matching in industrial markets (turbine generators) where price matching took the form of a contractual price-protection clause. Subjects may not have seen connection between industrial sellers in the case and their role as consumers. However, when put into the role of sellers, that connection may have become more apparent.

Price-matching signals are unique because unlike straightforward promotion signals, they do not advertise the amount of price cut, and may leave consumers with a false sense of security. Claims like "guaranteed lowest price" or "we won’t be undersold" are often misleading because such claims are seldom accompanied by systematic monitoring of competitor prices or lower shelf prices (Advertising Age, April 18, 1988, p. 79, 82). They are, however, a common tool of retailers. It is hoped, therefore, that future research will build on our work to gain a better understanding of consumer reactions to the phenomena, and devise methods to aid consumers correctly interpret such signals.

REFERENCES

Baye, Michael R. and Dan Kovenock (1994), " How to Sell a Pickup Truck," International Journal of Industrial Organization, 12 (1), 21-33.

Chen, Zhiqi (1995), "How Low is a Guaranteed Lowest Price?," Canadian Journal of Economics, 28 (August), 683-701.

Coughlan, Anne and Naufel J. Vilcassim (1989), "Retail Marketing Strategies: An Investigation of Everyday Low Pricing vs. Promotional Pricing Policies, " working paper, Kellogg Graduate School of Business, Northwestern University, Evanston, IL 60208.

Hess, James D. and Eitan Gerstner (1991), "Price-matching Policies: An Empirical Case," Managerial and Decision Economics, 12 (August), 305-315.

Levy, David T. (1994), "Guaranteed Pricing in Industrial Purchases," Industrial Marketing Management, 23 (October), 307-313.

Png, I. P. L. and D. Hirshleifer (1987), "Price Discrimination Through Offers to Match Price," Journal of Business, 60 (3), 365-383.

Salop, Steven C. (1986), "Practices that (Credibly) Facilitate Oligopoly Coordination," in Joseph E. Stiglitz and G. Frank Mathewson (eds.), New Developments in the Analysis of Market Structure, Cambridge, MA: MIT Press.

Sargent, Mark T. L. (1993), "Economics Upside-Down: Low Price Guarantees as Mechanisms for Facilitating Tacit Collusion," University of Pennsylvania Law Review, 141 (May), 2055-2118.

Zhang, John Z. (1995), "Price-matching Policy and the Principle of Minimum Differentiation," Journal of Industrial Economics, 43 (September), 287-299.

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