Consumer Information, Competitive Rivalry, and Price Setting: When Ignorance Isn't Bliss

ABSTRACT - The basic cost-benefit description of consumer search behavior which lays the foundation for much work in marketing is drawn from economics. However, consumer researchers have not considered the cost-benefit model as economists have intended it: as an explanation of the resulting price structure in competitive markets. This research takes an empirical look at how different levels of consumer information affect seller pricing under two conditions: (1) when major competitors respond quickly to a smaller competitor's price cut and (2) when they do not respond. The pilot test results indicate that better consumer information does narrow the dispersion of market prices. However, competitor rivalry drives prices down dramatically, even when consumers are poorly informed. The implications of the results for theory and future research are discussed.


Joel E. Urbany and Peter R. Dickson (1988) ,"Consumer Information, Competitive Rivalry, and Price Setting: When Ignorance Isn't Bliss", in NA - Advances in Consumer Research Volume 15, eds. Micheal J. Houston, Provo, UT : Association for Consumer Research, Pages: 341-347.

Advances in Consumer Research Volume 15, 1988      Pages 341-347


Joel E. Urbany, University of South Carolina

Peter R. Dickson, Ohio State University


The basic cost-benefit description of consumer search behavior which lays the foundation for much work in marketing is drawn from economics. However, consumer researchers have not considered the cost-benefit model as economists have intended it: as an explanation of the resulting price structure in competitive markets. This research takes an empirical look at how different levels of consumer information affect seller pricing under two conditions: (1) when major competitors respond quickly to a smaller competitor's price cut and (2) when they do not respond. The pilot test results indicate that better consumer information does narrow the dispersion of market prices. However, competitor rivalry drives prices down dramatically, even when consumers are poorly informed. The implications of the results for theory and future research are discussed.


Fundamental to consumer researchers' explanation of information search is economist George Stigler's (1961) "cost- benefit" model (Granbois 1977; Punj and Staelin 1983). Stigler's model leads to the conclusion that a market may contain buyers who are poorly informed; i.e., those for whom the benefits of gathering information do not exceed the costs. Consumer researchers, however, do not carry forward the major proposition underlying Stigler's and other economists' work in this area: that differences in competitive prices may exist even in a market for homogeneous goods because some consumers are poorly informed about what prices are available.

Economists have in the past 20 years been quite progressive in testing economic theory in laboratory settings (recent articles in Business Week and the Wall Street Journal attest to the growing importance of this work). This work has begun to appear at ACR conferences, particularly research addressing pricing behavior in markets with imperfectly informed consumers (Grether and Wilde 1984; Grether, Schwartz, and Wilde 1985). Grether et al. (1985) concluded their presentation by noting the great potential for marketing researchers and economists to learn from one another.

The present research presents a methodology to test whether prices will be lower when sellers believe consumers to be well informed than when they believe consumers to be poorly informed. We additionally extend the analysis of pricing behavior to consider competitive rivalry, which is not formally considered in the information economics literature. We discuss below the potential for competitive rivalry to distort or change competitors' knowledge about consumer behavior and lead (potentially unnecessarily) to price wars. The following section discusses the economic literature from which the current research questions were derived.


The concept of consumer information (i.e., consumer awareness of competitive offerings) has become central to the explanation of seller behavior in economics. Stigler's (1961) cost-benefit model proposes that consumers decide the number of sellers to sample by weighing the economic benefits of sampling against the economic costs. According to the theory, this weighing of search cost and benefit would lead some consumers to conduct little search which, in turn, would allow some sellers to charge higher prices. The cost-benefit logic is incorporated in all the more recent developments of consumer search theory in economics (e.g., Nelson 1970; Wilde 1980).

A major criticism of Stigler's work, however, is that his model provides no formal description of the seller-pricing behavior which leads to price dispersion. As Rothschild (1973) notes:

While his (Stigler's) model explains how customers should react to variability in price, it does not explain where this variability comes from or what, if anything, preserves it. (p. 1288)

Rothschild's frequently cited summary of economic models describing behavior when buyers and sellers have poor information has motivated work in economics to develop clearer specifications of how sellers set price when consumers are imperfectly informed. In particular, Salop and Stiglitz (1977) and Wilde and Schwartz (1979) present ground-breaking models of seller behavior in markets with imperfect consumer information. Each of these models explains specifically how the resulting distribution of prices in a market is a direct function of the percentage of consumers who are "shoppers" (i.e., samples more than 1 seller). Both models generally propose that sellers set prices by:

Estimating the percentage of consumers who are "shoppers,"

Estimating the demand curve (which is a function of the percentage of well-informed consumers), and

Determining the profit-maximizing price given the demand curve, the firm's marginal cost schedule, and capacity and profit constraints.

Key assumptions in these widely-cited economic theories are that (1) sellers are perfectly informed about consumer search behavior, (2) sellers intuitively incorporate their knowledge of consumer search behavior is estimating their demand curves and setting prices, and (3) competitive rivalry does not affect pricing decisions [Our consideration of competitive rivalry and its effect on pricing decisions in markets with imperfectly informed consumers is not intended as a criticism of these pioneering theoretical models. By analytical necessity, these authors had to make certain simplifying assumptions, including assumptions regarding competitive behavior. Rather than point out the limitations of economic theory, our aim is to broaden what we know about pricing behavior using the theory as a springboard.].

Important Empirical Questions

A consideration of the economics of information literature as it relates to sellers pricing behavior leads to the following empirical questions:

1. Are sellers' beliefs about consumer information-gathering as accurate as economic models assume?

2. Do sellers incorporate beliefs about consumer information-gathering into estimates of demand elasticity and pricing decisions?

3. Does consumer information gathering affect seller price setting as predicted under distinctly different conditions of competitive rivalry?

The current research examines the second and third questions. The following section considers the issue of competitive "conjecture" and considers how it may affect pricing decisions even in the face of a poorly informed consumer market.

Competitive Rivalry and Pricing

Competitive conjecture can be defined as the observation and anticipation of competitors' actions. This notion is reflected in models of competitive behavior and game theory (see Dolan 1981). It is obvious that competitors' observed and anticipated actions have an important impact on any sellers' decisions, but there exists little empirical evidence regarding when and how competitors' actions affect decision-making. Considering the pricing models discussed above an important theoretical and managerial question emerges: Is the urge to follow competitors' actions strong enough to distort sellers' beliefs about consumer behavior? A related issue is whether competitive rivalry focuses sellers so much on the competitive environment that they ignore their beliefs about consumer behavior.

A consistent finding reported in the agricultural economics literature has an important bearing on these issues. McCracken, Boynton, and Blake (1982) published comparative retail grocery prices in the Sunday editions of local newspapers in 4 test cities for 12 weeks. They measured the price of a market basket of goods across the experimental period in the test cities and four matched control cities. A clear pattern emerging from the study was that prices in the test cities (relative to the control cities) declined dramatically when the price information was published and increased again when the publishing of price was halted, a finding very consistent with the economics of information. It was found, however, that the price wars that resulted (which were so intense as to force the researchers to terminate the study early in one market) were due to competitors driving prices down even though consumer patronage behavior remained basically unchanged (Boynton, Blake, and Uhl 1983). In other words, consumers reported that the published price information had little effect on their grocery shopping behavior [Consumers' "underuse" of this information (relative to the experimenters' and retailers' expectations) is consistent with the literature in consumer behavior which suggests that consumers are not as motivated to be informed as many models of decision-making presume them to be (Olshavsky and Granbois 1979).]. The dramatic price declines were apparently due to competitors' mistaken belief that consumers had become better informed about grocery prices and were motivated to use that information in determining where to shop.

These results have been explained recently by Benson and Faminow (1985). They propose a spatial theory of pricing which suggests that consumer store-switching may be limited by distances between stores and that some external event (like the publishing of retail prices by a third party) affects sellers' conjectures about competitors' likely responses more than it affects consumer behavior. As such, McCracken et al. (1981) observed quick and desperate moves by retail sellers in the absence of consumer behavior patronage shifts. Benson and Faminow's theory provides an interesting contrast to information economics. It suggests that sellers may initiate changes in the structure of prices in anticipation of (or in response to) competitors' actions independently of better informed consumers.

The overriding point in that predictions of seller pricing behavior derived from models in which consumers are imperfectly informed may not hold under different conditions of competitive rivalry and conjecture. There is little question that a consideration of competitor behavior is fundamental to seller pricing decisions. This study empirically examines the separate (and, possibly interactive) effects of consumer information and competitive rivalry on seller pricing decisions.


Below we present each hypothesis and then explain the theory and logic from which it was derived. In the hypotheses the "seller" is faced with deciding whether or not to respond to a competitive price cut.

H1 - Sellers will be more likely to follow a competitor's price cut when consumers are believed to be well- informed than when consumers are believed to be poorly informed about competitive prices.

This hypothesis is taken straight from the logic of Stigler's (1961) cost-benefit model, which was more clearly developed later by Salop and Stiglitz (1977) and Wilde and Schwartz (1979).

H2 - Sellers will be more likely to follow a competitor's price cut when other competitors follow the price cut than when other competitors do not follow.

This hypothesis is derived on the basis of intuition and is consistent with a version of Benson and Faminow's (1985) model of price-setting in a competitive retail market. It is also consistent with Porter's (1980) framework in as much as competitive rivalry works to drive down industry profitability.

H3 - Alternative 1. Regardless of the level of competitive response to market price-cutting, sellers will be less likely to respond to that price-cutting if consumers are believed to be poorly informed about competitive prices (i.e., the H1 prediction is invariant to the level of competitive response).

H3 - Alternative 2. As competitors respond to an initiator's price cut with their own price cuts, the effect of buyer information on seller price-setting will diminish. That is, sellers will cut prices to the same low levels whether consumers are believed to be well informed or poorly informed.

According to the information economics theory, a seller's knowledge that consumers are poorly informed should lead to a lower likelihood of cutting price no matter how many competitors cut price. However, research cited above seems to indicate that competitive rivalry has a major impact on sellers' pricing decisions, possibly distorting sellers' beliefs about how well-informed consumers are. Alternative 1 under H3 is consistent with the information economics theory while Alternative 2 reflects the potentially "irrational" effect that competitive rivalry can have OD seller decision-making (Benson and Faminow 1985).


The research examines price-setting behavior via a case study in which information about consumer and competitor behavior is experimentally manipulated. Below we describe the case study, the sample, and design of the study.

The Case Study

In the case study the respondent plays the role of regional pricing manager for OURSTORE (a disguised retail grocery chain) who has to decide what prices to set for a group of products in the coming week. In the case, OURSTORE's major market has just been attacked by a relatively small share competitor (FEISTY) with an aggressive price cutting campaign. The competitive stores in the fictitious market (Anytown; a city of 800,000 which respondents are told is a real market) are all described in the case as fairly similar full-service retail operations. Differences in retail assortment, volume and profitability, clientele, and store layout are not described explicitly in the case to keep it simple. The case explains the competitive situation and presents the relative shares of the major competitors, which are as follows:


The case describes FEISTY's current price promotional blitz (which is reported to have begun on October 6) and presents competitive price information for eight products from August 25 and October 20 price surveys. The case then discusses consumer shopping behavior in the Anytown market, reporting results from recent consumer surveys conducted by a "major university and an industry organization both located in Anytown." Finally, the respondent is presented with the decision task: setting prices for the week of October 27 for the 8 products listed in the price surveys.

The Sample

The sample consisted of 68 masters students enrolled at a major state university. The students were all taking a marketing strategy course and were administered the case in a classroom setting. A structured questionnaire was attached to the case to obtain subjects' price recommendations and measure their beliefs about the Anytown market.

Experimental Design

The study involved a 2 X 2 design in which consumer information and competitor response were manipulated. The manipulations tool: the following form:

1. Consumer Information. Respondents were told in the case that several recent research studies have indicated that either 10 or 75 percent of consumers are active comparison shoppers in the market.

2. Competitive Reaction to FEISTY's Price Cuts. There are two levels to this manipulation: (a) neither LEADER nor OPPONENT have responded to FEISTY's price cuts by cutting price; or (b) both LEADER and OPPONENT have responded by cutting price. This information is reflected both in the case text and in the price surveys attached to the case. The four experimental conditions (and cell sizes) were (1) 10 percent comparison shoppers/no competitive price cuts (18); (2) 10 percent comparison shoppers/competitors cut prices (16); (3) 75 percent comparison shoppers/no competitive price cuts (17); and (4) 75 percent comparison shoppers/competitive price cuts (17).

The Questionnaire. The questionnaire first asked subjects to recommend retail prices for the eight products in light of the following wholesale prices provided in the questionnaire:


The questionnaire asked respondents to briefly justify the prices they had recommended and to respond to a structured question inquiring about a longer term pricing strategy recommendation. Respondents were asked to estimate 2 liter Coke quantity sold at various prices (discussed below) and then answered a series of agree-disagree items which measured their beliefs about the local market.

Selection of the Product Categories. It was felt that the number of product categories used should be limited to eight to reduce the complexity of the task. Further, generally "hot" specials items were selected for the study because these would be key candidates for price cuts in a competitive retail environment. The eight items used are ones normally falling into competitive price comparison lists and were selected from a listing of comparative product prices published in Progressive Grocer, which also provided common retail prices. The wholesale prices provided in the questionnaire were obtained from a local retailer.


Manipulation Checks

Respondents' perceptions of consumer information were measured with three "disagree-agree" items: "Most consumers in Anytown are well-informed about competitive grocery store prices," "Most Anytown consumers are NOT active users of retail grocery print advertising," and "Most consumers in Anytown will find out if your prices are higher than your competitors." In addition, respondents were asked to indicate their rating of Anytown consumer "info madness" on a 10 point scale ranging from "poorly informed" to "very well informed." Coefficient alpha for these items was .83, so the items were summed to form a scale (ISCALE) ranging in value from 3.5 to 20. [The 10 point scale was divided by 2 prior to summing so it would not have a disproportionate weight in the scale. Also, items were recorded to be consistent in direction. The same approach was used in the creation of two other scales (RSCALE and PSSCALE) described below.] The mean ISCALE score was 8.90 for the "10 percent comparison shopper" group and 15.04 for the "75 percent" comparison shopper group (p<.001, omega square = .57), supporting the consumer information manipulation.

The manipulation checks for the competitive response manipulation involved two disagree-agree items and a 10 point rating of competitors' responsiveness to the new competitive conditions. The two disagree-agree items were "OURSTORE's competitors have reacted quickly to FEISTY's new low price strategy" and "In response to FEISTY's new low price strategy, OURSTORE's competitors are fighting fire with fire." These three measures had an alpha of .78 and were summed to form a scale (RSCALE) which ranged in value from 2.5 to 15. The mean RSCALE scores were 5.13 for the "no competitive response" condition and 11.82 for the condition in which competitors followed FEISTY's price cuts (p < .001), omega square .74), indicating a substantial effect of the manipulation on subjects' perceptions of competitor responsiveness.

There were no significant interactions between the consumer information and competitive response manipulations on either ISCALE or RSCALE. Consistent with a basic test of manipulation validity (Perdue and Summers 1986), the competitive response manipulation did not affect ISCALE (p = .422) and the consumer information manipulation did not affect RSCALE (p = .248).

Perceived Price Sensitivity

Fundamental to economic theory is the notion that sellers' estimates of price elasticity of demand drive pricing decisions. Further, in models of price-setting when buyers are imperfectly informed, sellers' estimates of their demand curves depend largely upon their beliefs about consumer information. Our study examines this proposition as well as the possibility that competitor reactions affect sellers' beliefs about price sensitivity. Two approaches were taken to examine the effects of our manipulations on perceived price sensitivity of demand.

First, responses to belief statements assessing perceptions of price sensitivity were examined. Three disagree-agree statements ("Most Anytown consumers will shop elsewhere if your prices are higher than competitors' prices," "Consumers in Anytown are very responsive to price changes," and "Anytown consumers demand low supermarket prices") and a 10 point rating of Anytown consumers' price sensitivity were summed to form PSSCALE (alpha = .83, possible range 3.5 to 20). A significant main effect for the consumer information manipulation was discovered (PSSCALE means = 9.00 and 13.74 for the 10 percent and 75 percent groups, respectively, p < .001, omega square = .43), but no main effect for the competitive response manipulation was observed (PSSCALE means = 11.22 and 11.29 for the "no response" and "response" groups, respectively, p = .39). Clearly consistent with the information economics theory, beliefs about consumer information had a strong effect on sellers' estimates of price sensitivity. However, estimates of market pr cc sensitivity were not influenced by competitors' reactions to FEISTY's price cuts.

Subjects' estimates of price sensitivity were examined more explicitly for one product: 2 liter Coke. Subjects were told that OURSTORE had sold 1000 cases of 2 liter Coke during the week of October 20 when the Coke was prices at $1.79. They were then asked to estimate how much Coke would have been sold had OURSTORE priced the Coke at $1.99, $1.59, $1.39, $1.19, and $.99. In short, subjects were asked to estimate the demand curve. Consistent with the RSCALE results discussed above, the competitive response manipulation had no effect on the quantities estimated at each price. However, the consumer information manipulation did affect the demand curves estimated. At each price point below $1.59, the group believing that 75 percent of consumers were comparisons shoppers estimated significantly greater unit sales than the group believing that only 10 percent comparison shopped. The average are elasticity of demand between the $1.59 price point and the 5.99 price point was -1.02 for the 10 percent comparison shopper group and -1.34 for he 75 percent comparison shopper group (p, .05, omega square = .08). Again, perceived elasticity of demand for Coke was not increased by competitors' quick response to FEISTY's price cut (mean elasticities = -1.16 and -1.18 (p=.881), respectively, when competitors did and did not respond with price cuts). The interaction between consumer information and competitive response was not significant in its effect on the are elasticity of demand estimate.

In all, these results indicate that better consumer information led subjects to believe that the market was more price sensitive. However, estimates of market price sensitivity were unaffected by competitors' reaction to FEISTY's price cut.

Pricing Results

The eight prices recommended by each respondent were summed to form an index we labelled PINDEX. An important reference point here is that the sum of OURSTORE's prices during the week of October 20 (one week before the respondents' October 27 pricing decision) was $13.92. Figure A, which plots the mean PINDEX values by cell, indicates that all cells were substantially (and significantly) below that $13.92 level. This is not to say that the task encouraged subjects only to slash prices. Across the eight product categories, an average of 39 percent of the sample recommended that the October 20 prices by maintained for the week of October 27. The main effects of consumer information (F(1,61) = 10.26, p < .01) and competitor response (F(1,61) = 14.79, p < .01) were both significant in the predicted directions, with an overall omega-square of .36. The results support both H1 and H2 described above.



It is interesting to note that, in spite of the fact that the competitor response manipulation had no effect on estimates of consumer price sensitivity, that factor still had a dramatic effect on subjects' pricing decisions. ID fact, the competitive response factor accounted for somewhat more variance in pricing behavior (omega square = .22) than did the consumer information factor (omega square = .14). The fact that competitor response significantly affected pricing decisions under both conditions of consumer information are discussed below.

The interaction between consumer information and competitive response was not significant (p = .527), providing support for alternative 1 of Hypothesis 3 described earlier. In other words, the intensity of competitors' response did not change the result that better consumer information reduced the dispersion of prices in the market. We conclude that, consistent with the information economics theory, subjects' beliefs about consumer information affected both estimates of price elasticity and pricing decisions. Further, this effect was not diluted by competitors' response to FEISTY's price cuts.

Pricing When Consumers are Poorly Informed

Information economics theory proposes that sellers should be less willing to drop prices when consumers are poorly informed than when they are well informed about competitive prices. The results above show strong support for this proposition. A more specific and subtle interpretation of the theory is that, when consumers are poorly informed, market prices should remain dispersed even if competitors cut price. However, our results indicate that the dynamics of competitive rivalry appear to produce pricing behavior under conditions of poor consumer information that is different from expectations based upon the theory. Figure B presents the PINDEX results for the 34 masters students and 11 retail grocery executives from a separate pilot study who were presented with the case stating that only 10 percent of consumers were comparison shoppers.



The first thing to note in Figure B is that all the mean PINDEX scores are significantly lower than 513.92, OURSTORE's prices during the week of October 20. This, in itself, is inconsistent with the information economics theory which would propose that the subjects should hold off from price-cutting since consumers are poorly informed. The possibility exists that the respondents in the "10 percent shopper" condition may have thought that FEISTY's price cuts (which in the case occurred after the consumer surveys) generated more consumer search and therefore led to greater price sensitivity. However, these subjects' average post-task estimate of the percentage of comparison shoppers was not significantly different than the 10 percent figure given in the case.

Second, it is of interest to note that executives recommended markedly lower prices than did the students (fully $1.00 less for the eight products in each condition). This reflects the aggressive stance that most grocery chains have taken to pricing in the face of competitive price cuts, particularly for items for which consumers are believed to be price sensitive. On the whole, executives in this "poor consumer information" condition recommended that prices for the eight products be dropped by over $2.00 compared to the previous week's prices.

The most interesting result, however, is that competitive response affected price-setting even though consumers were poorly informed. For both groups of subjects, LEADER's and OPPONENTs quick reaction to FEISTY's price cuts led to lower prices than when LEADER and OPPONENT did not reduce prices. Competitive rivalry clearly affected pricing behavior and did so independently of consumer information. What seems inconsistent with the information economics theory, however, is that competitors' quick response to FEISTY influenced subjects' price-setting even when only 10 percent of the consumer market was described as comparison shoppers.


The general objective of research in this stream is to understand how information drives the behavior of buyers and sellers in the marketplace. The focus of the current study was how sellers set prices, a topic addressed more extensively in the experimental economics literature than in marketing in spite of its fundamental importance to our field. The case study scenario generated the following major results:

1. More informed consumers heightened subjects' estimates of consumer price sensitivity and led to more aggressive price-cutting in the face of a competitors' price cut. Interestingly, the response of other competitors did not moderate the effect of consumer information on price setting.

2. The aggressive price-cutting response of two competitors to FEISTY's low price blitz did not affect subjects' estimates of Anytown consumers' price sensitivity. In spite of this, subjects did drop prices more aggressively when the LEADER and OPPONENT dropped their prices than when the two competitors left prices the same. This raises an interesting question about classic economic theory which argues that seller price-setting depends fundamentally upon estimation of the demand curve

3. Even when consumers were said to be poorly informed, subjects still responded to FEISTY's price threat by cutting prices of the previous week. Prices were cut even more dramatically (in spite of the poor consumer information environment) when the two competitors responded to FEISTY's threat by dropping price. This further demonstrates the impact of competitor behavior on decision-making independent of other influences. Further, this finding is quite consistent with retailer behavior in the grocery industry today, where price is the major weapon in the battle for market share (Progressive Grocer 1986).

These conclusions should be tempered by the fact that the primary subjects were masters students rather than actual retail grocery price-setters. Pilot test results on the latter indicated similar patterns in price setting, although sample sizes were too small to undertake a full factorial analysis. Future studies should focus on the executives.

Economists for a number of years have made the intuitive argument that consumer information affects seller price-setting. The current study avoids the inherent difficulties of observing actual price-setting behavior by using a case study approach. This study supports the predictions of the information economics theory regarding seller price-setting behavior although, as noted above, some important questions about the theory are raised. The active price-cutting observed in is study, even in the face of poor consumer information, should not be interpreted as a demand artifact resulting from the focus on pricing issues in the case. The case clearly noted that the respondent should feel under no pressure from the home office either to cut or not cut prices. It can be argued that the future study of consumer behavior and the development of pricing models derived from either economics or marketing requires a better understanding of the behavioral tendencies (and perhaps biases) of price setters. Consideration of economic theory also reinforces the importance of studying consumer search behavior in marketing, particularly in the industry under study (the grocery industry). An important question raised by this and previous research is the following: is the fierce price competition in this industry driven by competitive "skittishness" or by a fundamental understanding of consumer demand (which is based upon knowledge of how consumers search)? As researchers of both buyer and seller market behavior, this question is worthy of our attention


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Joel E. Urbany, University of South Carolina
Peter R. Dickson, Ohio State University


NA - Advances in Consumer Research Volume 15 | 1988

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