Repositioning Dynamics and Pricing StrategyPaul B. EllicksonUniversity of RochesterSanjog MisraUniversity of RochesterHarikesh S. NairStanford UniversityJanuary, 2011yAbstractWe measure the revenue and cost implications to supermarkets of changing theirprice positioning strategy in oligopolistic downstream retail markets. Our estimateshave implications for long-run market structure in the supermarket industry, and formeasuring the sources of price rigidity in the economy. We exploit a unique datasetcontaining the price-format decisions of all supermarkets in the U.S. The data containthe format-change decisions of supermarkets in response to a large shock to their localmarket positions: the entry of Wal-Mart. We exploit the responses of retailers to WalMart entry to infer the cost of changing pricing-formats using a “revealed-preference”argument similar to the spirit of Bresnahan and Reiss (1991). The interaction betweenretailers and Wal-Mart in each market is modeled as a dynamic game. We nd evidencethat suggests the entry patterns of WalMart had a signi cant impact on the costs andincidence of switching pricing strategy. Our results add to the marketing literature onthe organization of retail markets, and to a new literature that discusses implications ofmarketing pricing decisions for macroeconomic studies of price rigidity. More generally,our approach which incorporates long-run dynamic consequences, strategic interaction,and sunk investment costs, outlines how the paradigm of dynamic games may be usedto model empirically rms’positioning decisions in Marketing.Keywords: Positioning, dynamic games, EDLP, PROMO, retailing, pricing, pricerigidity,;[email protected] usual disclaimer [email protected];

It is not necessary to change. Survival is not mandatory.– W. Edwards Deming1IntroductionE ective marketing strategy in a dynamic environment requires the ability to foresee theneed for change and a willingness to change direction. In marketing, large changes to someor all of a rm’s marketing apparatus are referred to as “repositioning.”Firms may reposition themselves along a variety of dimensions. Perhaps the most common (and visible) forms of repositioning are brand and product related. Recent examplesinclude Domino Pizza’s attempt to switch their reputation from fast delivery to high qualityand the repositioning of UPS from shipping to full o ce solutions (“What can brown dofor you?”). Other examples include adjustments to product lines, such as Hyundai’s recentmove into the luxury auto segment in the U.S. or Kodak’s long-delayed transition to digitalimaging. While product based decisions are clearly the most common form of repositioning,they are far from the only examples. Apple’s recent inclusion of third party retailers can bethought of repositioning their distribution strategy, while Proctor and Gamble’s adoption of“Value-Based Pricing” in 1992 (by reducing trade-promotions) was a repositioning of theiroverall pricing strategy (Ailawadi, Lehmann, and Neslin (2001)).A key aspect of repositioning decisions is that they are inherently dynamic. Sunk investments in positioning and reputation are often only partially reversible, and have signi cant long-term consequences for competitive market structure and future pro tability.Forward-looking rms must consider not only how consumers will react to their decisions,but whether their rivals will respond in kind. They need also take into account how today’sdecisions impact tomorrow’s options. For example, a promise to o er every day low priceshas commitment value, but limits a rm’s ability to respond to macroeconomic ‡uctuations.Taken together, these facets of the decision environment suggest that positioning decisionsin Marketing should be viewed as dynamic games. Furthermore, by framing decisions inthis way, we are able to construct a framework that allows us to structurally estimate thebene ts and costs of repositioning.In this paper we examine the repositioning of supermarket rms’ pricing strategies.The organization of retail supermarkets for CPG goods in the US is broadly split betweenEDLP (Every Day Low Price) and PROMO (or promotional) price positioning strategies.11PROMO is also referred to as “HiLo.”2

EDLP stores charge a low regular price per product with little temporal price variation,while PROMO stores are characterized by higher regular prices, punctuated by frequentprice promotions or “sales.” The propensity of stores to choose EDLP or PROMO pricepositioning is motivated by both demand- and cost-side considerations. On the demandside, choosing PROMO over EDLP o ers an opportunity for supermarkets to intertemporally price discriminate, by using price cycles to sell di erentially to consumers of varyingprice information, loyalty, stockpiling costs or valuations (Varian (1980); Salop and Stiglitz(1982); Sobel (1984); Lal and Rao (1997); Pesendorfer (2002); Bell and Hilber (2006)).Further, the frequent price variation under PROMO creates an option value to consumersto visiting the store more frequently by reducing their average basket size per trip (Belland Lattin (1998); Ho, Tang, and Bell (1998)). On the cost-side, EDLP enables retailersto reduce inventory costs, to better coordinate supply-chains, and to reduce stock-out riskby smoothing the demand variability induced by frequent sales. The choice of EDLP orPROMO is an important strategic choice faced by retailers that a ects their price image,with signi cant long-term implications for pro tability and local market structure (Ellicksonand Misra (2008b)). We analyze repositioning decisions in the context of this choice.The empirical goals of this paper are to measure the revenue and cost implications tosupermarkets in oligopolistic retail markets of changing their pricing formats. Repositioningfrom EDLP to PROMO (or vice versa) involves signi cant revenue changes and sunk costs.On the revenue side, in addition to the price discrimination motive, consumer aversion tochanges in price positioning may reduce long-run demand, and make rms inertial in theirpricing policies (Anderson and Simester (2010)). On the cost-side, much of costs associatedwith advertising the new positioning; with the man-hours involved in updating inventoryand supply-chain systems for changing pricing strategy; and with purchase of new pricingand demand-management software to manage promotional activity, are sunk. The long-rune ects on the demand-side imply that changing price positioning requires dynamic considerations. The sunk nature of costs on the supply-side implies the format change decision isonly partially reversible, and is therefore a dynamic decision (Dixit and Pindyck (1994)).The sunk aspect also implies format change has commitment value. With commitmentvalue, format changes may have long-term in‡uence by a ecting market events such as theentry and exit behavior of other rms, far into the future. Most retail markets in the US alsotend to be oligopolistically competitive and concentrated, with a few (3-5) dominant playerscontrolling the market, irrespective of its size. Both imply that strategic interaction may3

be an important consideration for the format change decision. To accommodate these keyconsiderations, we present an empirical framework that treats format change as a dynamicproblem with sunk investment. Strategic interactions are accommodated by formulatingthe model as a dynamic game of incomplete information with entry and exit, in the spiritof Ericson and Pakes (1995). We outline methods for identifying the key constructs of themodel using the available data, and propose new ways to infer the structural parametersof the game using recently developed methods for two-step estimation of dynamic games(Aguirregabiria and Mira (2007); Arcidiacono and Miller (2008)). We also show how toincorporate revenue information (a continuous outcome) into the estimation procedure inan internally consistent manner, while accounting for the dynamic selection induced by theco-determination of these with the discrete-choices, thereby extending the work of Ellicksonand Misra (2008a) to a dynamic environment.The incorporation of strategic interaction is important to the estimation of repositioningcosts. For instance, in a competitive market, a supermarket may be reluctant to switch fromPROMO to EDLP because it anticipates that price competition may be toughened if a rival rm, currently doing PROMO, shifts to EDLP in response to its action. In the absenceof this control, the persistence induced on pricing strategy by such strategic interactionwould be falsely interpreted as repositioning costs. This is the additional complication thatis encountered when measuring switching costs for rms. This is accommodated in ourframework by allowing rms to form beliefs about the reactions of others in the market,a ecting their choices of pricing formats. In our Markov Perfect equilibrium, beliefs andactions are consistent, and will be functions of the state variables faced by the rm. We arethus able to recover the beliefs of the rms directly from the data for use in estimation, bynonparametrically projecting the observed actions of the rms onto the state vector. Ouranalysis also has the advantage that we don’t just measure inertia in pricing policy, butalso provide a conceptual underpinning for why such inertia arises. Anticipation of strategiccompetitive response in the future is one source of pricing inertia which is embedded in themodel. Additionally, by treating format change as an dynamic decision in the presence ofuncertainty, we implicitly allow rms to have an option value from waiting. Intuitively, rmshave an incentive to wait to see the realization of uncertain future shocks to pro tability,and to optimize pricing policy as their uncertainty resolves. This is another source of pricinginertia that is a naturally embedded in our framework.Our data, which covers the entire census of retail supermarkets in the US for over a4

decade, includes a period of intense change in the retail industry: the introduction of WalMart supercenters. This has particular relevance for inferring repositioning costs, since theentry of Wal-Mart supercenters serve as large shocks to the competitive structure of localmarkets, inducing a large number of format switches and a host of exits. Our identi cationof switching costs rests on a revealed preference argument similar to that of Bresnahanand Reiss (1991): if we see that a rm switched its price positioning, it has to be that thepro ts (in a present-discounted sense) from the switch were higher than those without it.As we observe revenues, we can decompose this restriction on pro ts into a restriction onthe costs of the change. Combining this with the model of the industry and the variationacross markets enables us to relate these restrictions to market and competitive conditions.Our results imply the costs of changing pricing formats are large and asymmetric. Inparticular, for the average store in our data, a change from EDLP to PROMO requiresa xed outlay equivalent to roughly half of the typical per period revenue. On the otherhand, a switch from PROMO to EDLP requires outlays four times as large, providing aclear explanation for why EDLP was never uniformly adoptedit is simply too expensiveto be viable in most markets. We also nd evidence for signi cant heterogeneity in the costsacross markets, holding out scope that geographic segmentation in rm’s price positioningstrategies may be worth considering. The magnitude of the values we estimate also implythat these costs have large implications for long-run market structure. Consistent withexisting Marketing evidence (cited below), we nd overwhelming evidence that PROMOproduces higher revenues. For the median store-market, PROMO yields an incrementalrevenue of about 6.4M annually relative to EDLP. We also nd that the entry of Wal-Marthas large and signi cant e ects of the propensity to switch pricing formats.Our approach is closest in structure to Sweeting (2007), who estimates the dynamiccosts radio stations face when changing music formats. Substantively, the question we askis di erent as there is no role for consumer pricing in radio (since radio music is free);further, we allow rms to exit, in the event that shifting to a new pricing strategysticking with the current oneoris unpro table. In our model, the margin from staying inthe market versus exiting identi es the per-period xed costs of operation; while the marginfrom changing a format, conditional on staying in the market, identi es format-switchingcosts.Substantively, the empirical evidence on the relative attractiveness of EDLP versusPROMO strategies is scarce. In a study from one retailer, Mulhern and Leone (1990)5

report sales increased in a switch from EDLP to PROMO. In the strongest evidence availableso far, randomized pricing experiments involving the Dominick’s stores conducted by theUniversity of Chicago (Hoch, Dreze, and Purk (1994)) nd that category by category EDLPis not preferred relative to PROMO (revenues declined when categories, but not stores, wereswitched from EDLP to PROMO). The literature is still lacking an accounting of how thesetrade-o s change when the long-term economic costs of switching are incorporated. Inour data, we nd that a switch from EDLP to PROMO increases revenues as well as theprobability of store-exits, suggesting that format change cost considerations are qualitativelyimportant to an audit of price positioning strategies. Our paper is also broadly related toan empirical literature that has descriptively documented the e ects of Wal-Mart entryon incumbent rms (e.g. Singh, Hansen, and Blattberg (2006); Basker and Noel (2009);Matsa (Forthcoming)), and to an ambitious recent structural literature that has modeledthe entry decisions of Wal-Mart as dynamic (but abstracting from strategic interactions;Holmes (Forthcoming)), or as jointly determined across geographies (but abstracting fromdynamics as in Jia (2008) or Ellickson, Houghton, and Timmins (2010)). Our approachis also related to the recent empirical literature in Marketing of applying static discretegames to entry models of supermarket supply (Orhun (2006); Vitorino (2007); Zhu andSingh (2009)); to demand under social interactions (Hartmann (2010)); and to productintroductions (Draganska, Mazzeo, and Seim (2009)). Finally, our focus on measuringdynamic switching costs for rms complements the recent literature in Marketing that hasconsidered dynamics induced by consumer-side switching costs for demand with rewardprograms (Hartmann and Viard (2008)) and for rm’s pricing decisions (Dubé, Hitsch, andRossi (2009)).More generally, our analysis is related to a new literature that exploits the richnessof Marketing scanner panel datasets to understand the frequency and nature of microlevel price changes, and to explore the extent to which prices are “sticky” (Eichenbaum,Jaimovich, and Rebelo (Forthcoming); Campbell and Eden (2010); Kehoe and Midrigan(2010); Chevalier and Kashyap (2011)). These studies investigate high-frequency pricechanges under PROMO. The point we make here is that a hitherto unrecognized largersource of price rigidity is the commitment by the retailer to an EDLP or PROMO strategy.In particular, the choice by a retailer to follow EDLP implies that nominal prices can lie onlyin a restricted set. The sense in which prices are sticky then is the fact that the restrictionto this set reduces the retailers ability to respond to macro shocks. Hence, the choice of6

price positioning has bite for understanding price rigidities in the economy. When viewedthrough this lens, our empirical exercise can also be thought of as measuring an adjustmentcost of changing prices.The paper is organized as follows. Section 2 provides background on the supermarketindustry, as it appeared in the late 1990s. Section 3 introduces our formal model of retailcompetition, while section 4 outlines our empirical strategy and econometric assumptions.Section 5 describes the dataset, establishes key stylized facts, and details our approach toidenti cation. Section 6 contains our main empirical results, along with a discussion of theirbroader implications. Section 7 concludes.2Supermarket Pricing in the US: The Turbulent 90sOur analysis focuses on the strategic pricing decisions made by supermarket rms in the midto late 1990s.2 This was a period of signi cant change for the supermarket industry. Conventional supermarket chains faced intense competition from the rise of new store formatsand innovative entrants. At the forefront was Wal-Mart, which built its rst supercenter(a combination discount store and grocery outlet) in 1988, opened its 200th outlet in 1995,and would operate over 1000 supercenters by 2001. Club stores, such as Sam’s Club andCostco, which each had roots in the 1970s, also expanded rapidly during this time, withSam’s opening 350 outlets between 1995 and 2000 and Costco opening 93. Limited assortment chains, such as Aldi and Save-A-Lot, were also gaining market-share, particularly inlow income areas and inner cities. While the specter of the internet still lay around thecorner, a merger and acquisition wave had dramatically increased the size of many chains.At the heart of many of these threats lay the perception that limited service, thinnerassortments and “every day low pricing”created enormous cost savings and increased credibility with consumers. EDLP, together with a limited product assortment, o ered thepromise of more predictable demand, reduced inventory and carrying costs, fewer advertising expenses, and lower menu and labor costs. Larger scale was thought to go hand inhand with lower prices. Much of this perception was driven by the success of Wal-Martalone, which leveraged technical sophistication in IT with buying power to squeeze suppliersand tighten margins, attaining out a dominant position in the retailing sector and forging2This section o ers some preliminary context for our analysis and application. Later, in §5, we describeour dataset in more detail and articulate how these trends manifest themselves in patterns of revenues, entry,exit, and pricing format changes in the data.7

an indelible perception as a low-cost leader. Many of the strategic decisions made by theincumbent supermarket chains were geared toward competition with Wal-Mart.While the impact of Wal-Mart on retail competition is undisputed, many observers assumed that the EDLP format would also come to dominate the supermarket landscape,ignoring both the signi cant sunk investments in repositioning necessary to implement itand the o setting bene ts of having frequent promotions (e.g. the ability to price discriminate, more frequent visits leading to more impulse purchases, and the fact that manyconsumers simply prefer to see things “on sale”). While Wal-Mart has continued its growthin the supermarket industry, we now know the EDLP revolution did not come to pass. Ourempirical analysis is aimed at understanding why. To do so, we seek to decompose thereturns to adopting the EDLP or PROMO format into three components: revenues, operating costs, and repositioning costs. We nd that while EDLP pricing provides signi cantcost savings, it is very expensive to implement (i.e. the repositioning costs are signi cant).Moreover, it leads to a signi cant reduction in revenues relative to PROMO pricing.3ModelIn this section, we describe our structural model of supermarket competition and pricingformat choice. There are two types of rms, Wal-Mart and conventional supermarkets (e.g.Kroger, Safeway). We will generically refer to the rst type as Wal-Marts and the secondtype as supermarkets. Supermarket rms are assumed to compete in local markets, takenhere to be zip codes, although we allow for some degree of cross-market competition inthe case of Wal-Mart. Supermarket rms choose whether or not to enter a given market,and if so, what pricing format to adopt, either EDLP or PROMO. We also model theentry decisions of Wal-Mart, but assume that every Wal-Mart is EDLP, consistent withboth the data and their stated business model. Once they have entered, a supermarket rm’s dynamic decisions include whether to continue o ering the same format, switch tothe alternative (and pay a switching cost), or exit the market entirely. Wal-Marts neitherexit nor change formats. For tractability, we assume that rms make independent entryand format decisions across local markets, but allow for correlation and economies of scaleand scope by allowing xed operating costs to depend on past choices the rm has madeoutside these local markets.The dynamic discrete game unfolds in discrete time over an in nite horizon, t 1; :::; 1:Firms compete in M distinct local geographic markets (m 1; ::; M ). For ease of notation,8

we suppress the market subscript in what follows. For each market/period combination, weobserve a set of incumbent rms who are currently active in the market. We further assumethe existence of two potential supermarket entrants per period, who choose whether or notto enter the market in that period and, if so, what pricing strategy to adopt.3 If they choosenot to enter, they are replaced by new potential entrants in the subsequent period. WalMart may also choose whether to enter the market each period and, if they do enter, theydo so in the EDLP format. Let N denote the total number of rms (both Wal-Mart and thesupermarkets) making decisions in each market each period. Within N , the set of active rms are called incumbents, and the remaining rms potential entrants. We suppress thedistinction between potential entrants and incumbents in the general set-up of our model,but will revisit this when we introduce the empirical framework. Within each market, weindex rms by i 2 I f1; 2; :::; N g : Firm i’s choice in period t is given by dit 2 Di ; whilethe actions of its rivals are denoted dtid1t ; : : : ; dit1N; di 1t ; : : : ; dt . The support of Di isdiscrete, and dependent on rm type. For incumbent rms, dit can take three values, [Exit,do EDLP, or do PROMO]. For potential entrants, dit can take three values, [Stay out of themarket, Enter with the EDLP pricing format, or Enter with the PROMO pricing format].For Wal-Mart, dit can take two values, [Stay out of the market, or Enter with the EDLPpricing format].Decisions and payo s depend on a state vector, which describes the current conditions ofthe market, as well each rm’s operating status and pricing format. Following the standardapproach in the dynamic discrete choice literature, we partition the current state vector intotwo components, one that is commonly observed by everyone (including the econometrician)and one that is privately observed by each rm alone, making this a game of incompleteinformation. We denote the vector of common state variables xt , which includes marketdemographics such as population, and a full description of each player’s current condition.The key endogenous state variables included in xt are each rms’ current pricing formatand whether they are active at the beginning of each period t.In addition to the common state vector, each rm privately observes a vectortdit ;which depends on its current choice and can be interpreted as a shock to the per periodpayo s associated with making that choice, relative to maintaining the status quo.4 Once3A normalization on the number of potential entrants of this sort is standard in the dynamic entryliterature, as it is not identi ed without additional information.4This can be interpreted as either a shock to revenues or to costs. We can allow for one, but not both. Wewill interpret the "-s as shocks to revenues, which enables us to account for selection on these unobservableswhen we incorporate revenue data in our estimation procedure.9

again following standard practice, we make two additional assumptions. The rst is additiveseparability (AS ): the unobserved state variables enter additively into each rm’s per periodpayo function. The second is conditional independence with independent private values(CI/IPV ): conditional on each rm’s choice in period t, the ’s do not a ect the transitionsof x; the ’s are also independently and identically distributed (iid) across time and overplayers. We further assume that ’s are distributed Type 1 extreme value (T1EV), withdensity function g( ).Given assumption AS, the per period (‡ow) pro t of rm i in period t; conditionalion the current state, can be decomposed asxt ; dit ; dti dit : The pro t functiontis superscripted by i to re‡ect the fact that the state variables might impact di erent rms in distinct ways (e.g. own versus other characteristics). Assuming that rms movesimultaneously in each period, let P dt i jxtiitchoose actions dt conditional on xt . Sinceas follows,denote the probability that rm i’s rivalsis iid across rms, P dt i jxt can be expressedP dt i jxt IYj6 ipj djt jxt(1)where pj djt jxt is player j’s conditional choice probability (CCP). These CCP’s represent“best response probability functions”, constructed by integrating rm j’s decision rule (i.e.strategy) over its private information draw, and characterize the rm’s equilibrium behaviorfrom the point of view of each of its rivals (as well as the econometrician). For now we willtake these beliefs as given, later deriving them from each player’s dynamic optimizationproblem and the conditions for a Markov Perfect Equilibrium (MPE).Taking the expectation ofixt ; dit ; dtiover dt i , rm i’s expected current payo (netof the contribution from its unobserved state variables) is given by,ixt ; dit Xidt 2DP dt i jxtixt ; dit ; dti(2)which accounts for the simultaneous actions taken by each of its rivals. We assume thatstate transitions follow a controlled Markov process. We will now specify that process andconstruct each rm’s value and policy functions.Let F xt 1 xt ; dit ; dtirepresent the probability of xt 1 occurring given own action dit ;current state xt ; and rival actions dt i : Note that we can estimate F (:) semiparametricallyfrom the data as all the elements, xt 1 ; xt ; dit ; dt10iare directly observed. The transition

kernel for the observed state vector is then given by,f i xt 1 xt ; dit Xidt 2DP dt i jxt F xt 1 xt ; dit ; dti(3)Given the CI/IPV assumption maintained earlier, the transition kernel for the full statevector is,f i xt 1 ;it 1xt ; dit ;it f i xt 1 xt ; dit g(it 1 )We are now in a position to construct each rm’s value function, optimal decision rule(strategy), and the conditions for an MPE. Assuming that rms share a common discountfactor , rational, forward-looking rms will choose actions that maximize expected presentdiscounted pro ts,E(1Xtix ;di di tjxt ;i)(4)where the expectation is over all states and actions. They do so by choosing a policyfunction (strategy) ; a mapping from states to actions, to sequentially maximize (4). ByBellman’s principal of optimality, we can de ne rm i’s value function, the expected presentdiscounted value of pro ts from following ; recursively as,Vti (xt ; t ) maxditSincetixt ; dit E Vt 1 (xt 1 ;tit 1 jxt ; dt )(5)is unobserved, we further de ne the ex ante value function (or integrated valueifunction), V t (xt ), as the continuation value of being in state xt just beforeVit (xt )is then computed by integrating Vti (xt ; t ) over t ,ZiV t (xt )Vti (xt ; t )g( t )dttis revealed.(6)Finally, to connect values to choices, we de ne the choice speci c value function vti (xt ; dt )as the present discounted value (net of t ) of choosing dt and behaving optimally from periodt 1 on,vti (xt ; dit )i(xt ; dit ) ZiV t 1 (xt 1 )f (xt 1 jxt ; dit )dxt 1(7)Notice that we have now employed the transition kernel in evaluating the expectation.Given that the ’s are distributed T1EV, equation (7) reduces to,Ziiiiiijxt 1f i xt 1 jxt ; dit dxt 1 v xt ; dt xt ; dt v i xt 1 ; dt 1ln pi dt 1(8)11

whereiis Euler’s constant and dt 1represents an arbitrary reference choice in periodt 1 (this reference choice re‡ects the requirement of a normalization for level; for thefull derivation of this representation see Arcidiacono and Ellickson (2011)). Note thatby normalizing with respect to exit, which is a terminal state after which no additionaldecisions are made, the continuation value associated with this reference choice can nowbe parameterized as a component of the per period payo function, eliminating the needto solve the dynamic programming (DP) problem when evaluating (8). Avoiding the fullsolution of the DP is critical in our setting, as our underlying state space is essentiallycontinuous. Alternative methods would either involve arti cial discretization of the statespace (to allow transition matrices to be inverted) or a parametric approximation to thevalue or policy functions. The current approach requires neither.The choice speci c value function, the dynamic analog of a static utility function, determines the CCPs that will ultimately form the likelihood of seeing the data. In particular, rm i’s optimal decision rule (i.e. strategy) at t solves,it (xt ; t )and integrating overt arg max vti (xt ; dt ) dtt(9)yields the associated conditional choice probabilities,exp v i xt ; ditpi dit jxt Pexp v i xt ; dit(10)dit 2Diwhich were employed earlier in constructing the state transition kernels. Assuming that rms play stationary Markov strategies, we follow Aguirregabiria and Mira (2007) in representing the associated Markov Perfect Equilibrium in probability space, requiring each rm’s best response probability function (10) to accord with their rivals’beliefs (1). Whileexistence of equilibrium follows directly from Brouwer’s xed point theorem (see, e.g., Aguirregabiria and Mira (2007)), uniqueness is unlikely to hold given the

that suggests the entry patterns of WalMart had a signi–cant impact on the costs and incidence of switching pricing strategy. Our results add to the marketing literature on the organization of retail markets, and to a new literature that discusses implications of marketing pricing decisi