Retailer reactions to competitive price changes

Retailer Reactions to Competitive Price Changes PETER R. DICKSON* The Ohio State University

JOEL E. URBANY* University

of South Carolina

Despite the importance of understanding and predicting competitive reactions in oligopoly theory, empirical research on the topic is sparse. We examine whether retail decisionmakers’ pricing reactions conform to the asymmetric conjecture specified in the classic kinked demand curve theory: that firms will tend to follow competitors’ price cuts but notfollowprice increases. Factors which likely moderate this reaction pattern are discussed and examined via a survey which presents managers with a case study in which they must determine whether or not to respond to a competitor’s pricing initiative. The results do generally support the theory’s assumption about pricing reactions, but also indicate that reactions are moderated by item price sensitivity and influenced by the behavior of other competitors in the market. Response to price cuts is immediate in some cases, but response to price increases can only be motivated if other competitors follow the initiator first. We consider why these effects vary across products and how market learning may be inhibited by certain reaction tendencies.

The need for managers to account for the expected reactions of rivals in strategy decisions is fundamental to classic theories of oligopoly (Dolan 1981; Hirschleifer 1988; Pindyck and Rubinfeld 1989), game theory, (Moorthy 1985; Kreps 1990; Kadane and Larkey 1983; Urban and Star 1990), and prescriptive work on competitive strategy (Porter 1980). While the classic models of competition assume that the decision-maker (DM) is fully informed about both rivals’ reaction patterns and market elasticity (see Hirschleifer 1988; Friedman 1983; von Stackleberg 1952; Fellner 1949), it is more likely that the decision-maker will be uncertain about both buyer and competitor responses to pricing initiatives (Simon 1982; Urbany, Dickson, and Key 1990). An interesting example of this uncertainty is presented by Cassady (1963), who describes an all-out price war on bread in the Corvallis, Oregon market

Peter R. Dickson is the Crane Professor of Marketing, Max M. Fisher College of Business, The Ohio State University, Columbus OH 43210. Joel E. Urbany is Associate Professor of Marketing, College of Business Administration, University of South Carolina, Columbia SC 29208.

Journal of Retailing, Volume 70, Number 1, pp. 1-21, ISSN 0022-4359 Copyright Q 1994 by New York University. All rights of reproduction in any form reserved.

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in 1950. The warfare began with one independent grocer attempting to sneak a two-cent price advantage and ended a week later with all stores in the area nearly giving away their bread (e.g., $.Ol-$.03 per loaf). The initiator of this series of events clearly did not anticipate the severity of the price rivalry which would result. Much research addresses buyer response to price changes (cf. Monroe 1990; Winer 1988), yet descriptive work which examines competitors’ reactions to rivals’ pricing initiatives is sparse. In short, there is still much to learn about “how and why firms perceive, interpret, and react to competitive activity” (Weitz 1985, p. 233; see also Morgenroth 1964; Joskow 1975; Rao 1984). In this paper, we examine whether retail grocery price-setters’ reactions to competitive price changes are consistent with the “follow price cuts, don’t follow price increases” reaction pattern assumed in the classic kinked demand curve theory (KDC) (cf. Sweezy 1939; Hall and Hitch 1938). Below, we first discuss the theory and its detractors and explain why the study focuses on the retail grocery industry. In a departure from the usual approach of studying the history of prices in an oligopoly market, we then employ survey methodology to explore pricing reactions. The results indicate not only that managers are generally more responsive to price cuts than to price increases, but also that this tendency is moderated by other factors.

The Kinked Demand

Curve Theory: Follow Down,

Not Up (FDNU)

In an oligopolistic setting, the sales and profit outcomes of a firm’s pricing initiatives clearly depend upon the reactions of competitors. The KDC theory (Hall and Hitch 1938; Sweezy 1939) may be the only theory of competitive behavior which provides an explicit prediction of how competitors will react to a price change in such a market, holding that executives believe rivals will follow a price cut yet will not follow a price increase. This conjecture produces an “imagined” demand curve with a kink at the current price, bounded by a less elastic portion for lower prices and a more elastic portion for higher prices. While it continues to appear in leading textbooks (e.g., Hirschleifer 1988; Kreps 1990), the KDC theory’s ability to explain price rigidity has been hotly contested.’ Reid (1981) provides a thorough review of the empirical evidence regarding the KDC theory, identifying Stigler (1947) and Primeaux and Bomball (1974) as key representatives of the objective studies examining the FDNU rule. These authors present evidence that reaction patterns observed in eight different industries fail to conform to the FDNU conjecture. The results of such objective tests of the FDNU rule are intriguing but limited in that they are unable to account for the beliefs of the decision-makers (Reid 1981). Consequently, it is not clear whether the observed pricing patterns are produced: (1) because DMs do not believe in the KDC effect or (2) DMs do believe in the theory and implicitly collude to circumvent its effects (cf. Efroymson 1955). More importantly, the failure to find consistent FDNU behavior may be explained by the fact that competitive reactions (and, therefore, the shape of the imagined demand curve) are moderated by a number of factors, including primary and secondary demand2 growth or elasticity (Gatignon, Anderson, and Helsen 1989; Dolan 1981; Morgenroth 1964); the

Retailer Reactions to Competitive Price Changes

3

reactions of other rivals (Urbany and Dickson 1991), business conditions (Nowotny and Walthers 1978; Sweezy 1939); product differentiation (Stigler 1947); the number, size, and power of competitors (Stigler 1947); competitive information (Cohen and Cyert 1975); and stage of product life cycle (Simon 1982). Given the complexity of controlling for such factors in objective tests of the FDNU reaction pattern and the obvious importance of the DM’s perceptions, we examine these issues via a survey which presents a case study to managers. We now explain why we focus on the grocery market.

Retail Food Pricing: Attention to Competition

The retail grocery industry provides an interesting domain in which to examine the PDNU reaction pattern. Price has traditionally been the predominant basis of competition in this industry (Progressive Grocer 1987), although primary demand elasticity is generally low (a prerequisite for FDNU behavior; see Morgenroth 1964). Similar to behavior observed in other industries (Gatignon et al. 1989), firms in the grocery industry often react to new market entries (or new market strategy initiatives) with aggressive pricing, given the apparent belief that price has a relatively higher elasticity than other marketing tools. This is illustrated by events in Indianapolis in 1983-84, when the entry of Cub Foods (a warehouse operation) led to a price war, a $20 million loss for market share leader Kroger, and a major predatory pricing suit (Supermarket News 1983a; 1983b; 1983~; Grocer’s Spotlight 1984). However, three years later in Columbus, Ohio, a price war did not break out when Cub opened stores (Marks 1987), despite the similar presence of the market share leader Kroger (who chose not to cut prices) and a consumer population almost identical demographically (Progressive Grocer’sMarket Scope 1988). This leads to the conclusion that the Indianapolis price war was competitor- rather than consumer-driven. More formal evidence (Boyton, Blake, and Uhl 1983; Benson and Faminow 1985; Urbany and Dickson 1991; Hess and Gerstner 1991; Urbany et al. 1990) similarly suggests that price competition in the retail grocery industry is determined predominantly by discretionary seller behavior, which often produces heated battles in which competitive concerns may outweigh or color information about consumer behavior (see O’Conner 1986). The careful attention which executives give to the behavior of competitors suggests that the retail grocery industry is an appropriate arena in which to study pricing reactions.

HYPOTHESES

As discussed earlier, the KDC hypothesis is that firms will tend to follow price cuts and refrain from following price increases. The hypotheses described below address how two factors-secondary demand elasticity and rivals’ reactions-may moderate such reaction tendencies. Both of these factors are natural determinants of pricing behavior (DeVinney 1988) and, as discussed below, each has a clear conceptual logic underlying its potential moderating effect on pricing reactions. In addition, consumer information search (the key

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of secondary demand elasticity in this study) and competitive “reactiveness” are both being used increasingly in explaining firm behavior (cf. Porter 1980; Gatignon 1984; Nagle and Novak 1988). determinant

Demand

Elasticity as a Moderator

of Competitive

Reaction

The FDNU rule is most likely to be useful in accurately predicting competitive reactions in an oligopolistic industry with inelastic primary demand and elastic secondary demand. In particular, the latter condition is vital as the DM must believe that customers will switch their shopping quickly in response to price cuts (thereby necessitating retaliation when a competitor cuts price) and will also switch away from a competitor who, alone, raises its prices. Morgenroth (1964) studied an industry facing such conditions and found pricing patterns consistent with the FDNU rule. Similarly, Kroger’s aggressive price response to Cub in Indianapolis was apparently based on the assumption of very high secondary demand elasticity, while its limited price response in Columbus indicates either that that assumption had been revised or that the company had revised its assessment of the consequences of losing customers to the new competition. Secondary demand elasticity is reflected in the degree of consumer search in a market. As the number of consumers who are informed about competitive prices increases, the oligopolist should be increasingly concerned about the chance that a price differential will be discovered (see Stigler 1961; Salop and Stiglitz 1977; Wilde and Schwartz 1979). Consistent with this, the FDNU conjecture requires the assumption that a significant percentage of buyers search actively and will respond to price changes. Alternatively, if consumers have higher search costs (i.e., a smaller segment comparison shops), rivals should have less motivation to react to a competitor’s price cut or to maintain low prices when a competitor has raised the price. Again, making the reasonable assumption that local grocery markets typically have inelastic primary demand, Hl follows: Hl:

The FDNU effect should be stronger in a market with high secondary elasticity than in a market with low secondary demand elasticity.

Response of Other Competitors

as a Moderator

of Competitive

demand

Reaction

What happens when a DM observes one competitor change price and then, before s/he makes any counter move, other competitors follow the initiator? In this scenario, a certain momentum may be created which motivates the DM to take some action (Urbany and Dickson 1991; DeSarbo, Rao, Steckel, Wind, and Colombo 1987). If an initiator cuts price and other competitors follow, the pressure for a remaining competitor to follow will grow (which would presumably strengthen the predicted “follow down” effect). Alternatively, a “‘band wagon” effect of other competitors following an initiator’s price increase may provide a hesitant competitor with sufficient justification to follow the price increase (Kahneman,

Retailer Reactions

to Competitive

Price Changes

Knetsch, and Thaler 1986). We hypothesize that the imitative moderates the FDNU reaction pattern as follows: H2:

5

response

of competitors

The tendency to follow an initiator’s price cut (“FD”) will be strengthened when other competitors have already followed the price cut. Similarly, the tendency to follow an initiator’s price increase will be greater when other competitors have already followed the price increase (i.e., the “NU” effect will be weakened).

As will be discussed below, the study reported here focuses on a specific industry (retail grocery) and examines via a survey how managers respond to the pricing initiative of a relatively small firm in a market with four primary players. The reader should note at the outset the potential difficulty of generalizing the current results beyond similar domains.

METHOD

The Participants

Respondents in the study were a nationwide sample of 174 managers and executives responsible for pricing decisions in the retail food industry. Respondents were identified using an industry trade directory (The Directory of Supermarket, Grocery, and Convenience Store Chains, New York: Business Guides, Inc.). Names/companies were randomly selected from all regions of the country. For each firm selected, the person with primary responsibility for pricing decisions was identified and contacted by telephone. This often required several calls. Those who agreed to participate (roughly 90 percent of those contacted) were asked to look for the research materials (a case study and questionnaire) in the mail the following week. Response was partly encouraged by a drawing among respondents who were willing to return the questionnaire with their business cards for several $100 cash prizes. Of 480 case studies mailed for this portion of the study, 185 (39 percent) were returned with 174 (36 percent) containing complete responses on all relevant variables. This response rate compares favorably with other recent studies of managerial behavior (e.g., Kotabe 1990; Noordewier, John, and Nevin 1990; Keith, Jackson, and Crosby 1990).

The Case Study

Respondents were asked to set prices for OURSTORE, a moderate share (22 percent) competitor in a mid-sized market (population = 800,000) which was faced with a recent pricing initiative from a smaller competitor (FEISTY; 10 percent share). The market was disguised as “Anytown,” although the case was patterned after a real situation in an actual market. The two other major competitors in the Anytown market were LEADER (40 percent

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TABLE1. Price Information A. Price Information

and Cell Sizes

Presented in the Case Study

“Up” Condition: FEISTY Increases Prices

“DOWN” Condition: FEISTY Cuts Prices June 22 Prices OURSTORE April 20

OURSTORE

June 22 Prices

FEISTY

OURSTORE April 20

OURSTORE

FE/STY $ .49

Bananas

$ .49

$ .49

$ .39

$ .39

$ .39

Corn Flakes

$1.89

$1.89

$1.59

$1.65

$1.65

$1.87

Bologna

$1.39

$1.39

$1.09

$1.19

$1.19

$1.45

Milk

$2.49

$2.39

$1.97

$2.09

$2.19

$2.39

Orange Juice.

$ .89

$ .a9

$ .69

$ .69

$ .69

$ .89

Mayonnaise

$1.99

$1.99

$1.59

$1.59

$1.59

$2.09

Coke

$1.79

$1.79

$1.29

$1.29

$1.19

$1.79

Coffee

$3.19

$3.09

$2.67

$2.75

$2.79

$2.99

Note: Prices for FEISTY on April 20 were not provided in the case. B. Cell Sizes Price Decrease

Price increase None Follow

L&O Follow

None Follow

L&@ Follow

Low search (1 O/25%)

35

32

16

17

High search (75%)

16

20

15

23

Note: a. L&O = Leader and Opponent

share) and OPPONENT (23 percent share). All stores were described in the case as fairly similar full-service retail operations. In the case, FEISTY’s price initiative was described as either a set of price cuts or price increases, undertaken within the past two weeks. In addition to a description in the case of FEISTY’s price moves and competitors’ reactions, respondents were presented with competitive price information for two periods: two months prior to FEISTY’s new strategy and the week immediately past (see Table 1A). The final section of the case discussed consumer shopping behavior in the Anytown market and presented the respondents with their task: to recommend OURSTORE’s prices for eight products for the coming week. The case study was developed based upon theory and judgment. Except for the manipulation of moderators, we generally selected “levels” of other variables that would be consistent with past literature on the KDC and/or that appeared to be judgmentally sound. We selected a small number of firms given the oligopolistic nature of most retail grocery markets (Baumol, Quandt, and Shapiro 1964). A smaller firm was selected to be the price initiator rather than a market leader given the conceptual argument that rivals will automatically follow a leader’s price moves (Stigler 1947). Given no precedent, judgment was

Retailer Reactions to Competitive Price Changes

7

required to set up a market share structure so we chose one similar to that existing in a major midwestern test market city.

Experimental

Design

The study involved a 2 x 2 x 2 between subjects design in which the direction of FEISTY’s price change (up or down), consumer search behavior, and competitors’ reaction to FEISTY’s price change were manipulated. To manipulate consumer search, respondents were told that either a low percentage (10 to 25 percent)3 or a high percentage (75 percent) of consumers were active comparison shoppers in the Anytown market. Competitive reaction was manipulated with OPPONENT and LEADER either matching or not matching FEISTY’s price’changes (with a few pennies price differences included to add realism). The case study was pretested on some 20 supermarket executives as well as a sample of 68 MBA students to assess its realism. The pretest results led to only minor changes in the wording of the case and the questions. Seventy percent of respondents in the main study agreed that the Anytown market described in the case was representative of other markets they had seen.

Selection

of the Product Categories

Frequently purchased products were selected for the study as high-turn items that are often candidates for price cuts in a competitive retail environment. The items, selected from a comparative price listing in Supermarket News (1985), included bananas (1 lb.), Kellogg’s Corn Flakes (24 oz.), Oscar Meyer Bologna (8 oz.), whole milk (1 gallon), Minute Maid orange juice (frozen 16 oz.), Hellman’s mayonnaise (32 oz.), Coke (2 litre), and Maxwell House Coffee (1 lb. regular grind).

Determination

of the Prices

The price list presented respondents with prices for OURSTORE, LEADER, and OPPONENT two months before (April 20) and two weeks after (June 22) FEISTY’s new higher or lower price policy began. Respondents were asked to recommend prices for the week of June 29. In effect, two sets of prices were used in the study: a higher set and a lower set. In the condition in which FEISTY had cut prices, all competitors were at the higher prices (with a few pennies’ variation for realism) on April 20. The higher prices were developed around information obtained about normal industry prices. The June 22 prices showed and the text of the case explained that FEISTY was now pursuing an “everyday low price” policy (in the price cut condition). The everyday low price strategy is a common positioning strategy and provided a plausible justification for FEISTY’s change in pricing policy. FEISTY’s new

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Vol. 70, No. 1 1994

every day low prices were set with the objective of providing a noticeable contrast to existing market prices, 10 to 33 percent below the April 20 prices of the three competitors. Pretests indicated that such differences were perceived by respondents as providing a relatively strong challenge to existing stores in the market. In the condition in which FEISTY had increased prices, the price levels were basically reversed: OURSTORE, OPPONENT, and LEADER all were reported to have the low price points in the April 20 price check, and then, in June, FEISTY was reported to have raised its prices to the higher price level (see Table 1A). It was explained that FEISTY appeared to be backing off an everyday low price campaign. The retailer’s unit cost for each product, which was obtained from an industry manager, was listed in the questionnaire attached to the case which asked for respondents’ pricing recommendations as well as a number of manipulation check questions.

Dependent Variable

The dependent variable is a measure of the degree to which respondents moved toward FEISTY’s new price for each item. Since we compare response to a price cut and response to a price increase in the study (i.e., price movements in opposite directions), the actual recommended prices are an insufficient measure of price response. The variable REACTION measures the percentage by which the gap between OURSTORE’s and FEISTY’s June 22 price was reduced: REACTION = (1 - (June 29 gap / June 22 gap)) * 100; where June 29 gap = (OURSTORE’s June 29 price - FEISTY’s June 22 price), June 22 gap = (OURSTORE’s June 22 price - FEISTY’s June 22 price) Gap is the difference (in cents) between the OURSTORE and FEISTY prices. The June 22 gap for each item is a constant based on the June 22 prices provided in the case (OURSTORE’s June 22 price minus FEISTY’s June 22 price). The June 29 gap is the difference between the respondent’s recommended June 29 price and FEISTY’s June 22 price. A REACTION score of 100 percent means that the respondent fully closed the gap, i.e., that the respondent fully matched FEISTY (whether FEISTY went up or down). A score of between 0 and 100 indicates that the respondent reacted by moving price closer to FEISTY’s, but not all the way.4 A REACTION score of 0 percent indicates that the respondent recommended no change in price. A negative REACTION score indicates that the respondent actually widened the gap between OURSTORE and FEISTY on that item (e.g., cutting price in response to FEISTY’s price increase). In short, a higher positive REACTION score reflects a more aggressive following reaction to FEISTY’s initiative. The REACTION index has the same logic as Amit, Domowitz, and Fershtman’s (1988) empirical measure of conjectural variation, although their measure reflects an opponent’s belief about how its rival will respond to a change in strategy, while ours is a measure of actual recommended response.

Retailer

Reactions to Competitive

Price Changes

9

Plan of Analysis The data are analyzed via MANOVA and univariate regressions using contrast coding. MANOVA is used initially to provide for an overall assessment of experimental effects on the bundle of eight REACTION scores recommended by respondents (intercorrelations between these scores were high, ranging from -.31 to .81). The univariate model for each product is then examined via a regression model of the following general form: REACTIONi = a + bl*U + bz*C + b3*I + b4*UC + bs*UI + b6*CI + bT*UCI + ei,

(1)

where REACTIONi = REACTION

score for product i, and

U = .25 when FEISTY increased price; -.25 when FEISTY cut price;’ C = .25 when both LEADER and OPPONENT followed FEISTY’s price move, -.25 when they did not; I = .25 for high consumer search condition, -.25 for low; and UC, UI, CI, UC1 = interaction terms. The codes for U, C, and I above create orthogonal contrasts which represent the main effects. The interactions are calculated to complete the full set of seven contrast variables needed to reflect the eight experimental cells (see Cohen and Cohen 1975, pp. 195-207). Since the cell sizes vary (see Table 1B and footnote 3), there is some degree of correlation among the seven resulting independent variables, but not enough to create concerns about multicolinearity (maximum intercorrelation = .21).

RESULTS

Manipulation

Checks

Eleven items in the questionnaire (answered after the pricing recommendations) served as manipulation checks. Four items assessed respondent perceptions of consumer information search behavior (e.g., “Most consumers in Anytown are well-informed about competitive grocery prices,” alpha = .79, standardized and averaged to form ISSCALE) and four others assessed respondent perceptions of consumer price sensitivity (e.g., “Consumers in Anytown are very responsive to price changes,” alpha = .X5, standardized and averaged to form PSSCALE). Substituting the manipulation check measures for REACTIONi in Equation 1, we found that the consumer search manipulation significantly influenced both ISSCALE (t= 8.51,~ < .Ol) andPSSCALE (t= 7.49,p < .Ol), with the high search condition producing higher means as expected. Further, the interaction terms were not significant. The competitor response manipulation had no significant effect on ISSCALE or PSSCALE. Unexpectedly, the updown manipulation did significantly influence ISSCALE (t = 2.21, p

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< .OS), as respondents in the price increase condition believed that consumers were somewhat less informed (Min,,e = - .l 1) than did respondents in the price cut condition (Mcut = -.03). Given the smaller effect size for the up-down manipulation and the fact that PSSCALE showed no similar effect, however, this was felt to have little impact on the interpretation of results. Three items were used to measure perceived competitor response (e.g., “OURSTORE’s competitors have reacted quickly to FEISTY’s new price strategy.“). There were differences in the wording of items used for the price increase condition (alpha = .84) and the price cut condition (alpha = .90) since the conditions differed in the direction of response. The resulting scale (RSCALE) was significantly influenced by the competitor response manipulation in both the price increase (t = 9.96, p < .Ol) and the price cut (t = 12.19, p < .Ol) conditions. RSCALE was not influenced by the consumer search manipulation, the up-down manipulation, or the interactions. As a whole, these results suggest that the consumer search and competitor response manipulations successfully influenced respondent perception of the case facts.

TABLE2. Multivariate and Univariate Effects on REACTION Scores DEPENDENT

Overall MANOVA Wilks’ Lambda

Individual

VARIABLE = Percentage reduction in OURSTORE-FEISTY price differential between June 22 and June 29. u

c

.32a

.71a

I

.96

u*c

.70a

U*l

C*l

.95

.91c

1. Bananas

-.76a .lVh

.95

Adjusted R2

Product Effects

2. Corn Flakes

U*I*C

.14h

.55

.lVh

-.15h

.07

3. Bologna

-.2va

.34a

.15h

.l 7

4. Milk

-.77a

.2va

.27a

.64

5. Orange Juice

.40a

.20a

.19

6. Mayonnaise

-.28”

.33a

.lV

7. Coke

-.77”

.l 7a

8. Coffee

.llh

.21a

Notes: For individual products, only effects which are significant in both the MANOVA are presented. Coefficients are standardized beta weights. a. p < .Ol b. p < .05 c.

p < .lO

.60 .03 and the univariate regressions

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Retailer Reactions to Competitive Price Changes TABLE 3. Mean REACTION

Scores

A. Main Effects u

C

FEISTY Cuts Prices FEISTY Raises Prices 1. Bananas

None Follow

L&e

Follow

109

-3

56

2. Corn Flakes

49

78

44

75

3. Bologna

61

36

36

64

4. Milk

102

-61

70

12

57

5. Orange Juice

19

86

26

64

6. Mayonnaise

66

42

40

71

7. Coke

98

19

60

71

8. Coffee

50

59

40

66

8. Significant C*l Interaction

c None Follow

I Corn Flakes

C. Significant

L&O Follow

Low Search

32

79

High Search

64

71

U*C Interactions u

c Bologna

Milk

FE/STY Cuts Prices 52

9

L&O Follow

69

58

None Follow

98

L&O Follow Coke

FE/STY Raises Prices

None Follow

106

None Follow L&O Follow

-130 -7

93

4

102

31

Note: a. L&O = Leader and Opponent

Results for the REACTION

Measure

Table 2 reports the MANOVA results for the eight recommended product prices (using the eight REACTION scores as the dependent variables), as well as the significant regression coefficients for the individual items. Examining the General FDNU Effect. The FDNU effect predicts that the response to a price cut should be greater than the response to a price increase, where a more aggressive following response is reflected in a larger REACTION score. The MANOVA results indicate significant (p < .Ol) effects of the up-down manipulation (u>, the competitive response manipulation (C), and the U*C interaction. In addition, the C*Z interaction is marginally significant. Examination of the individual regressions shows that the up-down manipulation

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has a significant negative effect on the REACTION measure for five of the eight products. The negative coefficients for U are consistent with the general “follow down, not up” prediction, indicating that the REACTION index was significantly lower when FEISTY raised price than when FEISTY lowered price. In other words, for most of the products we find that respondents showed a greater response to a price cut than to a price increase. The results for two of the products (orange juice and corn flakes) indicate that, counter to the FDNU prediction, the respondents followed FEISTY more aggressively under the price increase condition. The means for these main effects are presented in Table 3A. The competitive response manipulation had a consistent positive main effect on the REACTION index for all eight products. This finding indicates that a “following” response was clearly influenced by the responses of other competitors in both the price cut and price increase conditions (see Table 3A). The consumer search manipulation was not significant in the MANOVA. The effects predicted in Hl and H2 are considered below.

H7: Stronger “Follow Consumers Search

Down,

Not Up” Effect When the Majority

of

Hl predicts that high secondary price elasticity produced by high consumer search is a requisite for the FDNU effect and that the effect would be less likely to occur when only a minority of consumers are reported to be active comparison shoppers. The overall U*I term was not significant in the MANOVA, failing to support the expected interaction between the consumer search and up-down manipulations. However, two findings (the latter unrelated to the consumer search manipulation) suggest some impact of perceived consumer information on pricing reactions. C*Z Interaction. As noted above, the C*Z interaction term was marginally significant in the overall MANOVA. The univariate analysis indicates that the C*Z effect occurs only for corn flakes. Simple contrasts of means (Table 3B) reveal that consumer search increases REACTION for corn flakes only when the other two major competitors do not follow FEISTY’s price change (F{ 1,80) = 4.48, p < .05). When LEADER and OPPONENT follow FEISTY’s price move, REACTION scores are uniformly high across the two consumer search conditions (F( 1,90} = 0.31, p > .lO), suggesting that the movement of both major competitors to a new price point motivated a response, regardless of the level of consumer search. Between-Product DifSerences. In the open-ended explanations of their pricing recommendations, respondents commonly mentioned item “visibility”(i.e., volume, traffic-generation) as a key concern in their pricing recommendations. Counts of the number of times each item was identified by managers as “highly price-sensitive” found that milk, Coke, and bananas accounted for 49 percent of all mentions.6 The regression coefficients for each item and the explained variances show clearly that the up-down manipulation had a much stronger effect on the REACTION index for these high-image items than for the others. The strong main effect of the up-down manipulation on REACTION for bananas is due to respondents’ following FEISTY when it cut price but not when it increased price (see the main effect means for U in Table 3A). In short, the pricing recommendations for bananas were precisely consistent with the KDC prediction that price cuts will be followed and price increases will

Retailer Reactions to Competitive

Price Changes

13

not be followed. Similar effects were observed for milk and Coke, but the U effects for these items (along with bologna) were moderated by the responses of FEISTY and OPPONENT. These interactions are discussed below.

H2: Greater Tendency to “Follow up” When Other Competitors

Had Responded

H2 proposes that the resolve not to follow a price increase would soften in the situation where fellow competitors followed FEISTY’s price increase, while the “follow down” tendency would be strengthened by competitors’ response. The significant main effects of the competitive response manipulation support H2 for all products. This result is important in demonstrating that the reaction of other competitors increases the response regardless of whether price has been increased or decreased. U*C Interactions. The competitive reaction manipulation moderated the up-down effect for milk, bologna, and Coke (Table 3C). The consistent theme in these interactions is that, when FEISTY cut prices, our managers’ pricing REACTIONS were not influenced by LEADER and OPPONENT’s response (simple contrasts: F{ 1,101) = 3.33, 0.41, and 1.76 for bologna, milk, and Coke, respectively, allp > .05). When FEISTY raised prices, however, other competitors’ response had a substantial impact on REACTION scores (simple contrasts: F{ 1,691 = 20.60,59.54, and 12.33, for bologna, milk, and Coke, respectively, all p < .Ol). In short, respondents were quick to follow FEISTY’s price cuts, even when FEISTY was the only firm to change price, yet tended to hold still when FEISTY alone raised price. The recommended prices for milk obviously represent an exception to this last conclusion and deserve further consideration. The interaction for milk is particularly telling about pricing tendencies in the retail grocery industry. Note that, consistent with the above results for bananas and Coke, respondents uniformly matched FEISTY’s price cut for milk, regardless of LEADER and OPPONENT’s response. However, when FEISTY had raised price, our managers were extremely aggressive in pricing milk. When LEADER and OPPONENT had not followed FEISTY’s price increase, respondents tended to react by cutting the price of milk (note the REACTION score of -130 in Table 3C). This price-cutting strategy typically involved dropping price to $1.99 or $1.89 from $2.19, apparently to break the $2.00 barrier and appeared to be undertaken to use the milk price as a means to loudly signal a low price position to consumers. Note that H2 proposed that the following response of other competitors would legitimate a price increase when FEISTY had raised its price. As Table 3C shows, however, the LEADER/OPPONENT response did lead to higher recommended prices in the price increase condition, but the original gap between OURSTORE and FEISTY remained nearly intact. As such, the observed U*C interaction for milk was not the result of other competitors’ responses creating a justification for OURSTORE to follow the price increase. Instead, it was due to price cutting in the face of FEISTY’s price increase when other competitors had not followed FEISTY. These responses to a rival’s price increase initiative (even more aggressive than the FDNU rule) emphatically highlight the problems of attempting to increase the price of a highly visible, high turn product category that is believed by rivals to be very price sensitive.

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DISCUSSION

The results of the study can be summarized 1.

2. 3.

4. 5.

as follows:

A significant negative effect of the up-down manipulation was found for five of the eight products, providing general support for the expectation that price cuts would be followed more readily than would price increases. Significant positive effects of the up-down manipulation for orange juice and corn flakes are counter to expectations and are discussed below. The low consumer search condition (which was intended to reduce perceived price elasticity) did not reduce the FDNU effect as expected. There appeared to be a natural separation between products, with milk, Coke, and bananas being singled out as particularly “visible” and price-sensitive items. The FDNU effect was strongest for these three products. The following response of other competitors (OPPONENT and LEADER) led to a greater tendency to follow FEISTY, whether it had cut or increased price. Conclusions 3 and 4 are qualified by significant U*C interactions for bologna, milk, and Coke. For these items, respondents followed FEISTY’s price cut to the same degree whether other competitors had responded or not. For bologna and Coke, they tended not to follow FEISTY’s price increase unless other competitors responded. In the case of milk, FEISTY’s price increases were met with substantial price cuts when LEADER and OPPONENT did not follow FEISTY up.

The sections which follow explore selected aspects of the results in greater detail,

Variability

between

Products

As noted above, the strongest up-down effects were obtained for milk, Coke, and bananas, apparently as a function of the price-signalling power of these items. While little research has been conducted on store price image (cf. Alba, Broniarczyk, Shimp, and Urbany 1994) industry executives firmly believe that certain high-volume products are critical bellwethers of a store’s price image (Cox and Cox 1990; Nagle and Novak 1988). As such, executives are highly sensitive to competitive differentials on these items. In a series of interviews we have conducted with retail grocery executives, for example, one manager noted that price image concerns demanded competitive prices on milk, as his company could not afford to look “like a 7- 11.” Other comments from these executives clearly reflected the belief that consumer price memory is focused on items they purchase frequently (,‘I think people know what [produce] is selling for . . . items they buy frequently”) and are promoted regularly (“the average consumer remembers about 8 products-we’ve trained them like Pavlov’s dogs to remember those prices”). These beliefs underly the need to be price-competitive on such items. Results for bologna and mayonnaise were consistent with the three items above, but were not as strong. They seemed to reflect more of a “shadowing” response in which managers followed price cuts partially, and raised prices in response to FEISTY’s price increases (particularly when LEADER and OPPONENT followed), but did not make up the entire

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competitive differential. It is possible that these items, because they have less a “commodity” status than milk, Coke,’ and bananas, are not perceived to be quite as price sensitive. As such, respondents were not compelled to completely match the price cuts or to hold price in response to FESITY’s price increases. Three products did not fit the FDNU mold. There was no effect of the up-down manipulation on coffee. More interestingly, the significant positive effect of U for orange juice and corn flakes indicates that respondents tended to follow price increases more readily than price cuts. This effect may be explained by the relatively strong national brands in these categories (Kellogg’s and Minute Maid). In addition, these items appeared to have lower perceived visibility, as reflected in the fact that they were designated as “highly price sensitive” less often in respondents’ comments (the two products together accounted for only 17 percent of such comments). In short, respondents may have been less reluctant to follow competitive price increases on these items because they perceived them to be somewhat less price elastic than the items identified as highly visible.*

Competitive

Momentum

The strongest and most consistent effect in the study is the competitive response effect, which produced greater following behavior for all eight products, in both the price cut and price increase conditions. In DeSarbo et al’s (1987) terms, the momentum created by FEISTY’s moves, followed by the reactions of OPPONENT and especially LEADER creates substantial pressure against any inertia characterizing OURSTORE’s pricing. Further, this finding raises a question about how food retailers learn about how individual items signal the overall price image of their stores. If competitors do not adopt a prudent wait-and-see strategy and, instead, immediately match price changes (particularly price cuts), then it is not clear how they learn about changes in the price-image signalling power of the price-reduced items.’ Moreover, if it is common knowledge that rivals will follow, then why are price reductions initiated? Our interviews with retailers suggest an answer. Intense price competition in particular product categories (such as milk) has become an industry convention, a way of coping with competing on price across 20-30,000 SKUs. This higher level of initiation-imitation price competition, fed by manufacturer promotion incentives, has very likely created higher consumer price sensitivity in certain categories and also in part created the significant store price image signalling power of certain items (for related evidence at the brand level, see Grover and Srinivasan 1992). The question of whether the industry has, through its conventions, chosen the categories where consumer price sensitivity is inherently highest (based upon dollar volume or lack of perceived product differentiation) is an interesting direction for future research.

Limited Effect of Consumer

Search

Interestingly, managers may learn from conventional industry wisdom about differential consumer price knowledge and sensitivity between products (e.g., milk, Coke, bananas vs.

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others). However, our results indicate that they do not intuitively respond to information about the extent of between-store search in a manner generally consistent with information economics (see Table 2, which shows limited effects of the consumer information manipulation). This suggests that the relationship between consumer search and competitive price differentials, as depicted in the theory, may come about by sellers “learning the hard way” via lost sales at the cash register that their prices are too high rather than explicitly considering consumer search behavior. An alternative explanation exists for the limited consumer search effects, however. It is possible that the hypothesized effect of consumer search did not emerge because respondents perceived even the “low search” condition to represent a significant enough proportion of consumers shopping to justify pricing reactions similar to those in the high search condition. Such aconclusion would have to be considered tentative, but is consistent with the contention that even a small proportion of searchers may be large enough to “police” the competitive marketplace (Wilde and Schwartz 1979; Bloom 1990).

Managerial Implications

The swiftness with which respondents matched price cuts on high volume, high visibility items in the study suggests that permanent or semi-permanent price cuts on such items should be avoided to prevent price wars. Without sources of cost advantage relative to competitors, it is unlikely that firms will be able to make permanent price cuts on such items profitable. Similarly, firms considering aggressive pricing strategies should consider the longer term impact of such pricing tactics on consumer price expectations and sensitivity. As noted above, competitors will be likely to respond on high turn “visible” items. Such competition will potentially focus customer attention on price and increase elasticity (see Krishna, Currim, and Shoemaker 1991) leading to longer term downward pressure on market prices.

LIMITATIONS

AND RESEARCH DIRECTIONS

A general limitation of our research is that we manipulated competitive pricing behavior and measured pricing reactions, leaving only the alternatives to hold, raise, or lower price. It is important to recognize that a firm may respond to a competitor’s price change by changing other elements of the marketing mix (see also Gatignon et al. 1989). Further, caution should be exercised in generalizing our results, since they are based upon a study of a subset of products drawn from thousands of items that grocery retailers can use to price compete with each other. In addition, the results cannot be generalized to markets with high primary demand elasticity or to situations in which a market share leader initiates a price change. Although the case filled an 8.5“ by 14” page, some information which may have been helpful to respondents was omitted, such as other characteristics of the firm, the history of price competition in the market, or immediate volume effects of FEISTY’s price initiative. It should also be noted that several other variables (discussed below) could be expected to

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affect pricing decisions directly or to moderate the effects of other variables. Finally, an interesting empirical question is whether other market share structures would have produced different results. While these omitted variables made our study more conservative, including them in future research will provide for more powerful and discriminatory tests of the theory.

Research Directions

There exists a continued need to examine managers’ competitive pricing decisions empirically as a basis for testing models of pricing behavior (cf. Monroe and Mazumdar 1988). Clearly, our research can be extended to other industries and, even within the grocery industry, can examine the many other factors which likely moderate pricing reactions and the intensity of price competition (Porter 1980). Most interestingly in the current context, more formal manipulations of product “visibility” or price sensitivity are needed, along with the study of how various characteristics of the initiating competitor (e.g., market leader vs. small share firm), the industry (e.g., market concentration, market share structure, product differentiation, cost structures), and the situation (e.g., the sequence of reactions) affect pricing reactions. In addition, it will be of interest to examine the trade-offs between price-signalling and profitability by providing a more systematic examination of how margins may affect pricing decisions. Such research could manipulate unit costs to examine how much managers are willing to squeeze margins to remain competitive. The interesting feature of the grocery industry is the fact that price-setters are acutely concerned with price image, as well as market share and profit outcomes. There may be other industries in which price image is as important. In extending this work, the generalizability of the factors moderating the FDNU tendency can be examined, leading to the development and testing of more general models of competitive reaction. Acknowledgment: The authors acknowledge the financial assistance of the Department of Sponsored Programs and Research at the University of South Carolina and the Crane Professorship.

NOTES

1. Stigler (1978) provides a review of the literature on the KDC theory and presents both a pointed criticism of the price rigidity explanation (following his 1947 piece) and a reprimand of the economists who continued to propagate the theory. 2. We use these terms in the textbook sense. Primary demand elasticity reflects the response of overall market sales to changes in price while a particular firm’s sales response to price changes is labeled secondary demand elasticity. 3. The “price cut” treatments described here were a portion of an expanded design which included 3 levels of consumer search (10,2.5, and 7.5 percent) and several levels of competitor reaction. In this larger design, we found no significant differences between the 10 and 25 percent consumer search

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treatment levels on any of the manipulation checks or the pricing recommendations. As a result, those conditions were combined. 4. To illustrate: Assume that a respondent faces a FEISTY price cut on bananas to $.39. OURSTORE’s current price (before the respondent’s recommendation) is $.49, so the June 22 gap is $. 10. If the respondent recommends a June 29 price of $.44, her/his REACTION score for bananas would be (1 - (.05/.10)) * 100 = 50%, i.e., 50% of the original gap has been eliminated. 5. The 2 x 2 x 2 design used here produces eight experimental “groups,” The main effect of the up-down manipulation is examined by comparing the four groups who saw FEISTY cut price with the four groups who saw FEISTY raise price. When evaluating a main effect such as this via contrast coding, codes are created so that each subset of u groups receives a code of I/u (to obtain the average across the groups), while the v groups to whom the comparison is made each receive a code of -l/v (Cohen and Cohen 1975, pp. 195-196). 6. Respondents used a variety of terms to communicate price sensitivity including “hot,” “image items,” “high volume items,” and so on. Illustrative comments include: “I cut prices on the bananas, whole milk, and Coke because, of the items selected, these represent the most volume,” and “bananas, milk, and Coke are the products most customers would buy . . so cutting the price of these products will help build an ‘image.“’ Sixty-four percent of the managers described variations of such a selective price-cutting strategy. Providing further confirmation of the perceived signalling power of these items, a recent survey found that managers believed consumers most frequently compare store prices on milk, meat (e.g., ground beef, chicken), produce, and soda (Kalapurakal 1993). 7. Coke is clearly not a commodity in the sense that it is one of several branded products in the soft-drink category. However, it can be argued that because of its very high purchase frequency, its recognizability, and the heavy emphasis on price promotion in the category (Fader and Lodish 1990), Coke has become a staple or basic item for many households, among whom its price is well-known. 8. Another potential explanation for the orange juice and corn flakes results is methodological. The process for setting the OURSTORE and competitive prices led to a situation where for three items (orange juice, mayonnaise, and corn flakes), OURSTORE had a negative June 22 profit margin in the condition in which FEISTY had increased prices. In the price cut condition, FEISTY’s new prices were below OURSTORE’s unit cost for orange juice, mayonnaise, corn flakes, and coffee. Taken together, this would suggest that, for three products at least, there would bc additional incentive to raise prices (and less incentive to cut) beyond that created by competitors’ actions. While plausible, this explanation does not account for the results for mayonnaise, which are consistent with the FDNU prediction. 9. In a forced choice question describing different general strategic responses to FEISTY’s move, only 11 percent of the respondents in the price cut condition chose a strategy of “wait and see how long competitors maintain their current strategy.”

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Received: January 1993;

Revised: November

1993