Standardizing Interactive Pricing for Electronic...

A b s t r a c tInteractive pricing, the subset of dynamic

pricing where buyers and sellers enter a

computer mediated price-negotiation pro-

cess, has stimulated academic interest ever

since the introduction of Internet-related

B2C and B2B applications. However, this

has not yet led to the widespread use of

standardized interactive pricing mechan-

isms within industrial applications. A

recent study suggests that applicants

expect the integration of interactive pri-

cing mechanisms into existing IT infra-

structure to be very costly due to high

customization efforts. The standardization

of interactive pricing should thus be a first

step towards enabling a wider use of these

mechanisms. Building on the classification

of the range of dynamic pricing methods,

we analyze existing business standards

that should be capable of describing

interactive pricing mechanisms. Our ana-

lysis reveals the shortcomings of recent

business standards which therefore require

the development of an enhanced model for

interactive pricing applications. Addressing

this issue we propose a model that

integrates a price communication language

with a process description format for the

customization of interactive pricing

mechanisms. The paper concludes with

three case studies illustrating the use of

our model.

Keywords: interactive pricing, dynamic

pricing, electronic business, procurement,

XML standards

A u t h o r s

Michael Schwind([email protected]) is assistantprofessor at the chair of BusinessInformation Systems and OperationsResearch at the Technical University ofKaiserslautern, Germany. His researchfocuses on dynamic pricing methods andmarket engineering, especiallycombinatorial auctions for resourceallocation and procurement processes.Oliver Hinz([email protected]) is aPostdoctoral researcher at the chair ofElectronic Commerce at the Goethe-University of Frankfurt, Germany. Hisresearch interests include online pricing,impact of social networks, economics ofInformation Systems andInterorganizational ElectronicCommerce.

Standardizing Interactive Pricing for

Electronic Business

MICHAEL SCHWIND, OLIVER HINZ, TIM STOCKHEIM AND MARTIN BERNHARDT

INTRODUCTION

Interactive pricing is one of the mostcrucial economic activities wheresellers and buyers engage in bargain-ing about the exchange of servicesand goods at a price level that bothbuyers and sellers accept (Bakos1998). Due to the rise of theInternet and computer-aided trad-ing processes, this activity has under-gone drastic changes over the pastfew years. Reduction of processingcosts associated with price differen-tiation, lower menu costs and newpossible methods of interacting with

trading partners online have enableda plethora of dynamic pricingmechanisms.

In the context of this paper wedefine interactive pricing as thesubset of dynamic pricing wherebuyers can actively influence the finalprice of a product or service byexchanging messages (i.e. bids) witha seller. Since interactive pricingis able to charge different buyersdifferent prices for identical pro-ducts, it allows sellers to price-discriminate, which could resultin higher returns for the sellers(Varian 1996). Yet buyers canprofit from interactive pricing, too.Priced-out of the market in a staticprice scenario, some buyer segmentscould be served at lower prices byinteractive pricing (Bakos 1998).However, interactive pricing is noteasy to implement due to the highintensity of communication required.As it gives rise to additionaladministrative costs, the necessityof enabling communication betweennegotiation partners could reducethe benefits of such pricing methods(Reinartz 2001).

The use of interactive pricingmechanisms is a common featureon multiple online marketplacessuch as eBay (http://www.ebay.com) or Amazon (http://www.amazon.com/auctions). Despite thesales and revenue potential of

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Tim Stockheim([email protected]) works forResco GmbH as consultant specializedon IT services and development. Hereceived his PhD degree at the Institutefor Information Systems at the Goethe-University of Frankfurt, Germany. Hisresearch interests include price-basedallocation methods and optimizationmodels in transportation logistics andsupply chain management.Martin Bernhardt([email protected])works for Kampmann, Berg & Partner asconsultant in the financial services indus-try. Having graduated at TechnicalUniversity of Karlsruhe, he obtained hisPhD at the Goethe-University ofFrankfurt, Germany. His work on inter-active pricing mechanisms and theirprofit-maximizing design has been pub-lished in various journals.

RESEARCH

Downloaded By: [Schmelich, Volker] At: 14:35 24 March 2010

interactive pricing, static prices remain the mostestablished pricing model in business and industry, as alarge-scale survey among German companies reveals.While many companies acknowledge its potential, costlyintegration into existing IT and organizational infra-structure remains a persistent obstacle to the large-scaleuse of interactive pricing functionality (Sackmann andStruker 2005).

The literature considers standardization a key driverfor the development of new functionality since it is ableto crucially reduce process and integration costs,especially for small and medium-sized companies (Beckand Weitzel 2005, Stockheim et al. 2006b). In order todescribe interactive pricing mechanisms a standardizedlanguage is helpful for different reasons: First, a standardcan help one to design interactive pricing mechanisms byoffering a toolset of standardized processes and designvariables. Further, the initiator (usually a seller, but inthe case of a reverse auction also possibly a buyer) maydesign e.g. an auction with different design variables andstore it for later use. A standardized description couldthen be handed over to an intermediary (e.g., anelectronic marketplace) with the mandate to conductthe auction in the intended way. Finally, a standardizeddescription could be communicated to potential bidders,announcing the rules for the interactive pricing mechan-ism. This could prevent misunderstandings and make acomputer-aided protocol possible, thereby dramaticallyreducing the administrative costs of interactive pricing.

Considering both the advantages of interactive pricingmechanisms and the need for standardization in order toenable their wider use even for small and medium-sizedcompanies, the aim of our paper is twofold: First, weidentify the gap between findings in academia and the e-Business standards used by practitioners. Second, weprovide a model based on the work of Wurman et al.(2002) for the standardization of interactive pricingmechanisms in e-Business to overcome these short-comings. Compared to alternative approaches thatcategorize electronic negotiation and pricing processes,like the ‘Montreal Taxonomy’ of Bichler et al. (2003)and Strobel and Weinhardt (2003), our model is closerto the technical implementation level. It is especiallytargeted for a direct translation into XML and thusmakes standardized use in different XML-frameworkspossible. In fact our model does not provide such a widedefinition of attributes and aspects of negotiationmechanisms as the ‘Montreal Taxonomy’ does; howeverthis is not relevant for the industrial application ofinteractive pricing, because we only omit attributes thatcan hardly be represented in an automated negotiationprocess, like e.g. social norms or cultural differences.Compared to Wurman et al.’s model (2002) however,our model supports a wide range of interactive pricingmechanisms that have not yet been standardized.Additionally, we leave the model open to further

extensions to foster an advancement of the system inpractice.

The remainder of this paper is organized as follows:We first motivate this work by synthesizing efforts madeby academia to categorize pricing and negotiatingprocesses in line with the aims of e-Business standards.As we detect that existing e-Business standards areinadequate, we then develop a model that closes the gapbetween research and business practice. As proof of ourconcept we apply the model and demonstrate its use inthree complex interactive pricing mechanisms: reversepricing, combinatorial auctions and the second-priceauction Google AdWords. The final section concludesthe paper with remarks and suggestions for furtherelaboration of our model in the context of industrialstandardization efforts.

INTERACTIVE PRICING – THEORY AND PRACTICE

Pricing mechanisms can mainly be classified into staticpricing (i.e. prices can only be changed by the seller inthe long-term following market fluctuations), anddynamic prices (prices are changed in the short-term).In our context, dynamic pricing denotes a flexible pricesetting process with respect to market dynamics (supplyand demand fluctuations), sectoral price discriminationaccording to the willingness-to-pay (W2P) of theindividual consumer (consumer group), inter-temporalprice discrimination as well as the capacity and inventoryconditions of the sellers’ facilities. Dynamic pricingresults either from an interactive pricing process (inter-active pricing) or a short term price setting processtriggered by sellers depending on observed consumerbehaviour (dynamic posted pricing) (Kauffman andWang 2001). Interactive pricing can thus be regardedas a special case of dynamic pricing (Elmaghraby andKeskinocak 2003).

Over the past few years dynamic pricing has becomeincreasingly important. Elmaghraby and Keskinocak(2003) provide three reasons for this phenomenon:

N the increased availability of demand data;N the ease of changing prices due to new technologies;

andN the availability of decision support tools for analyzing

demand data for dynamic pricing.

Interactive pricing in particular has recently becomeimportant as a subset of dynamic pricing. Due to thereduction of processing costs associated with pricedifferentiation and new methods of interaction withtrading partners, a steadily increasing number ofnegotiated prices can be seen in Electronic Commerce(Stroebel 2000). The growing use of online auctions asthe most widely used form of interactive pricingmechanisms (Bichler 2000) manifests this trend.Table 1 outlines the different types of dynamic pricing.

162 Michael Schwind, Oliver Hinz, Tim Stockheim and Martin Bernhardt & Standardizing Interactive Pricing for Electronic Business


Table 1. Outline of dynamic pricing methods

Criteria

Seller-buyer

interaction

Description of the

pricing mechanism

Response to market,

processing time and cost

Allocation and

pricing efficiency

Dynamic Pricing

Mechanism

Negotiation-Based

Pricing (NBP)

1 : 1 (1 : n) (n : 1) Bilateral negotiations lead to exchange

of contracts based on individually

bargained prices. Multiple parties

can be involved with alternating

partners.

Dynamics depend on repetition frequency

and possibility of renegotiation.

Processing time and pricing costs

are usually high.

Efficiency corresponds to parties’

negotiation talent and bargaining

position of the parties.

Reverse Pricing (RP) 1 : 1 Consumer places a bid (typically

below his W2P). Seller accepts bid

if threshold price is outbid. Threshold

price is unknown to buyers.

Dynamics depend on the threshold price

setting and the consumers’ reaction.

Low processing time and cost due

to simplicity.

Exploits the individual W2P of the

consumers. Due to the invisibility of

the threshold price, in-equality is

not perceived.

Auctions 1 : n n : 1 n : n Interactively determines the price and

allocation of goods and services

based on bids according to

predefined rules.

Dynamics depend on the iteration rate

and are low for continuous auctions.

Pricing time and costs are up to the

auction setup.

Allocation and pricing efficiency vary

with mechanism design. Mechanism

design literature discusses this topic

in detail.

Dynamic Posted

Pricing (DPP)

1 : 1 Differentiates prices according to a

classification of the buyers

according to their W2P.

Dynamics depend on the sellers’ learning

rate for the consumers’ reaction

function. Low pricing time if ANPs

are used.

Exploits individual W2P leading to higher

returns. Perceived inequality can cause

consumer disaffection.

Yield Management

(YM)

1 : n YM price differentiation follows a

fixed scheme. YM uses consumer

self-selection related to service

level and timing conditions

Dynamics strongly relate to the W2P

learning rate and the quality of the

YM model. Costs are in mid-range

due to mature YMS.

Targets high revenues and achieves

satisfying allocation if pricing classes

and contingents are appropriate.

Electronic

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Since the approaches we have found in the researchsuffer from several shortcomings, we want to address thiswith our model for interactive pricing. Taxonomies likethe ‘Montreal Taxonomy’ focus on a broad abstractcategorization of negotiation processes including exo-genous rules, but do not allow translation directly intoan XML vocabulary. At the same time, descriptivemodels for interactive pricing in the literature focussolely on common auction mechanisms. Wurman et al.(2001, 2002) are the first to present a parameterizationof the Auction Design Space for the AuctionBot andshow that this parameterization can easily be translatedinto XML by providing an adequate XSD. Lochner andWellman (2004) present an advance on the AuctionBotparameterization that is capable of dynamically reactingto events in the course of the auction process by usingthe rule-based language AB3D.

The Descriptive Auction Language (DAL) proposedby Rolli et al. (2006) also takes on the direction of adynamic language while extending the parameterizationspace of the aforementioned approaches. DAL followsthe notions of a separate Auction Reference Model(ARM) that provides the foundations for structuringauction mechanisms (Rolli and Eberhart 2005).

Our interactive pricing model is based directly on theidea of Wurman et al. (2001, 2002) but incorporates awider range of pricing mechanisms at the cost ofomitting those exogenous rules suggested by the‘Montreal Taxonomy’ that can not be described in anXML vocabulary as already mentioned above. Since theprimary goal is to provide a standardized set of rules forinteractive pricing in the first stage of our modelproposal, we do not introduce dynamics into ourlanguage. Figure 1 depicts the differences between ourmodel and the academic approaches already discussed.The x-axis depicts the increasing complexity and level ofabstraction of the different interactive pricing frame-works in multi-attribute aspects of negotiations. Ourmodel provides a high level of standardization (y-axis)

like the ‘Auction Design Space’ of Wurman et al. andsupports a plurality of pricing mechanisms (z-axis) whichis also addressed by the ‘Montreal Taxonomy’.

Although more and more products are sold throughinteractive pricing, standardization seems unable to keeppace with this fast development. In the following sectionwe examine widely used e-Business standards in terms oftheir ability to model interactive pricing. This evaluationis mandatory to determine the gap between needs andpotential of e-Business standards.

In the late nineties various initiatives to identifycommon workflows in the area of B2B and to workout semantics to describe these workflows were started.Some of these initiatives focus on a particular targetgroup, whereas others are suitable for a broaderaudience. Therefore, the standards are not only sub-stitutes but can be used complementarily (e.g. as is thecase for the ebXML Framework and the UniversalBusiness Language). Due to their diverse focus, thestandards can be broken down into three majorcategories: Frameworks, functions and verticals.

Frameworks provide specifications for structuringXML messages between parties for exchange withinand among industries and probably receive the mostpublic attention (Kotok 2000). They are not restrictedto semantic declarations, but provide methods for theentire communication process, enabling intersectoraldocument exchange flow. An impressive number offrameworks have emerged, some of which are BizTalk,eCo, Open Applications Group (OAGIS), ElectronicBusiness XML (ebXML), Commerce XML (cXML) andRosettaNet.

Functions can be used as a pattern for a specificbusiness process that crosses industry boundaries. Theclass of functions includes the Common Business Library(xCBL), Guideline XML (gXML), Information andContent Exchange (ICE) Protocol, Universal BusinessLanguage (UBL) and XML/EDI Repositories.

Verticals are used for standardization within anindustry or within a holistic value chain. Many industrieshave created their own standard covering their specificneeds which has led to a plethora of conflicting oroverlapping specifications.

Since it has the nature of a business vocabulary, theprocess of interactive pricing should be encapsulated in afunction or vertical. However, some of the frameworksalso include more detailed descriptions of businessprocesses. For example RosettaNet’s latest version 2.0of PIP3A2 (Rosetta.org 2007) considers dynamicposted prices, while interactive pricing has not yet beenspecified. Normally, frameworks like the BizTalkFramework are purely specifications for documentexchange flow and thus focus on very basic informationstructuring and on transmission mechanisms. Thedescription of superordinate business transactions isusually omitted by frameworks.

Figure 1. The model for interactive pricing in contrast to relatedframeworks



The eCo Framework project started by CommerceNetin 1998 with Commerce One as primary corporatesponsor can be split up into an architectural specificationand a semantic specification. The architectural specifica-tion separates different functions like market, interactionand services into layers. In contrast, the semanticspecification provides a sample set of business docu-ments that can be used in the eCo Framework. Therecommendation (eCo 2007) only makes use of staticprices. Similarly, cXML (commerce eXtensible MarkupLanguage) developed by leading e-Business-companieslike Ariba and Sterling Commerce, is a plain XMLstandard aiming to cover a wide range of businessprocesses. Therefore, the degree of detail is rather low.Prices are defined as price per unit on a product level andcan be summed up on a higher level (cXML.org 2007).

The United Nations Centre for Trade Facilitation andElectronic Business (UN/CEFACT) as expert forstandardization and the Organization for theAdvancement of Structured Information Standards(OASIS) as expert for XML-specifications started thestandardization project ebXML (Electronic BusinessXML) in 1999, aimed to create a single global electronicmarket (OASIS 2007a). The aim of ebXML is to modelprocesses, describe the exchange of documents andspecify the basic components of business documents.The pricing model has not yet been determined butexamples make use of very simple pricing models(OASIS 2002). OASIS has recently started to promotethe function vocabulary UBL to extend ebXML withXML documents describing common business transac-tions.

Although UBL can be used within the ebXMLFramework, it can be applied uncoupled from ebXMLand, vice versa, ebXML does not rely on the vocabularyof UBL. The function vocabulary has been created as anexchange format for distinct e-Business-Frameworks,since vertical industries have created their own XMLstandard. UBL contains a sophisticated pricing model soas to implement a high level of inherent flexibility.Nevertheless, this function vocabulary assumes what is

called a BasePrice and does not make it possible todescribe the price as result of an interactive process(OASIS 2007b).

The latest version of xCBL (XML Common BusinessLibrary) (4.0) offers attributes for describing the pricingscheme, thus allowing prices to be differentiated basedon time or transaction currency. Furthermore, differenttypes of prices can be specified, such as StandardPrice,PromotionalPrice, DiscountPrice, and prices dependingon quantity scales (xCBL.org 2007).

Our survey reveals that all these e-Business standardsare based on static prices and that some of the moresophisticated standards allow one to describe dynamicposted prices. Prices can then be modified usingmultipliers depending on order amount, sales channelor billing currency. Hence, second and third degreeprice discrimination can be modelled.

Table 2 presents the pricing capabilities of the businessstandards examined, based on the following criteria:

N Relative: Relative price changes by multiplication ordivision.

N Conditional: Dynamic posted prices driven by seg-mentation, e.g. transaction currency, country, orderamount, sales channel, or validity period.

N Interactive: Specification of interactive pricingmechanisms.

Kelkar et al. (2002) give a more detailed insight intopricing models in different catalogue standards andprovide a generalized model which does not includeinteractive pricing. For a more detailed comparison offrameworks see Dogac and Cingil (2001). Meanwhile,most frameworks have been improved but have notusually undergone a significant change in focus.Nevertheless, nowadays neither the frameworks northe functions support interactive pricing, whichsuggests that standardization has failed to include thisimportant feature for contemporary trading processes.The large-scale use of interactive pricing mechanisms istherefore not yet encouraged by existing businessstandards.

Table 2. Evaluation of business standards based on pricing capabilities

Pricing capabilities

Business Standard Initiator Version Relative Conditional Interactive

cXML Ariba and others 1.2 2 2 2

ebXML OASIS 2.0 not def. not def. 2

RosettaNet RosettaNet modular + + 2

eCo CommerceNet 1.0 2 2 2

xCBL Commerce One, SAP and others 4.0 2 + 2

UBL OASIS 2.0 + + 2

Electronic Markets Vol. 18 No 2 165


A MODEL FOR INTERACTIVE PRICING

This section provides an overview of the properties thatdefine an interactive pricing mechanism. Due to ourfocus on electronic pricing, we only consider automatedsystems based on Internet technology. One field relatedto such a classification is mechanism design, where anenormous amount of research has been conducted formany years (Bajari and Hortacsu 2004, Bichler et al.2003, Kannan and Kopalle 2001, Pinker et al. 2003,Stroebel and Weinhardt 2003). However, insightsgained in academia are rarely used in applied e-Commerce or e-Business since practitioners often focuson simplicity when designing pricing mechanisms.

To compare different mechanisms, a systematicclassification using their attributes is mandatory(Bichler et al. 2003). Figure 2 provides an overview of

attributes and categories of attributes that are relevantfor interactive pricing mechanisms. Besides the primaryobjective of classification, it can also be used within amodelling tool or as a framework for a descriptionlanguage, e.g. XML. The ‘Montreal Taxonomy’, whichtries to accomplish similar objectives to those of theapproach presented, distinguishes between exogenousand endogenous, as well as explicit and implicitclassification criteria (Strobel and Weinhardt 2003:149) and focuses on the explicit endogenous rules ofthe mechanisms. Our model, presented in Figure 2,combines attributes from the ‘Montreal Taxonomy’ aswell as the work of Wurman et al. (2001, 2002) andfocuses on the explicit, endogenous criteria. Four basicfeature groups are identified: participation, bidding,clearing, and information policy. These policies containthe rules that define the mechanism. While dashed lines

Figure 2. Interactive pricing design space



indicate optional categories in Figure 2, the rules of mostcategories can be combined. The contents of thecategories and subcategories are detailed in the nextsubsections.

Participation policy

The participation policy restricts which roles are given tohow many participants in an interactive pricing process.The first two categories are buyer cardinality and sellercardinality, which classify the number of participants onthe demand and the supply side. The simplest form is acardinality of one, which occurs in one-to-one pricenegotiations on both sides and in classical auctions onthe one side, e.g. in the English Auction on the sellerside. If the admission of participants is restricted to aspecific group of persons or organizations, the setting islabelled as closed. By contrast, if in general each agentis allowed to participate in the auction or negotiation, itis open with respect to buyers and/or sellers. A classicalEnglish Auction would therefore be called one-to-many(open) or one-to-many (closed) depending on whetherthe group of participants is restricted or not. Moreover,in exchanges (or double-auctions) that usually have amany-to-many cardinality participants may be restrictedto being disjoint, i.e. either a seller or a buyer.

In addition to the direct implication of possiblebidder-fluctuations in the negotiation process, thecardinality of buyers and sellers reveals whether thesupply or demand is fixed or not, i.e. when participatingin a one-to-many auction the bidder knows the availableitems ex-ante. Furthermore, the effort to monitor themarket’s participants, which increases the chance ofsuccessful participation, increases with the number ofparticipants.

Most of these systems require the existence of anintermediary or, more technically, a server to ensure thatthe process is in compliance with rules arranged ex-ante.If the pricing mechanism is an auction, the intermediaryhas the role of the auctioneer and sends imperativemessages to the bidders. Furthermore, an intermediarycan act as a trusted third party and suggest solutions tothe price negotiating agents. A complete decentraliza-tion of such an interactive pricing process is possible, i.e.up to a degree where no more central instances arerequired.

Bidding policy

The bidding policy defines the bidders’ and sellers’ spacefor bargaining actions with respect to the pricingprocess. Expressiveness defines the bidding language.The aim of the bidding language is to express a functionof the bidders’ W2P in a structured way (Nisan 2006).In the most simple form, these are unit prices (for a

quantity of one item), which can then be summarizedunder the class of price-quantity schedules. A price-quantity schedule is a stepwise specification of variousquantities, and according to the level of prices a bidderoffers to buy or sell. Alternatively, the bidders’ W2Pcould be expressed by continuous price functions, whichcan simplify the bid structure in contrast to price-quantity schedules. In the case of infinitely divisiblegoods, e.g. fluids or bidding on using resources withincontinuous time windows, continuous price functionsmay be obligatory. Combinatorial expressions are apowerful means to express causalities in bids, whereasthe impact of combinatorial bids (or bundle bids) on thecomplexity of the mechanism depends on the number ofdependencies that exist between the items (usuallyexpressed as super- or subadditivity – see Case 2). Thetrade-off between the expressive power of the bids andthe resulting computational complexity of the allocationproblem can be reduced by allowing additional bidconstraints, e.g. capacity limitations in transportationscenarios or providing a list of prices to choose fromrather than letting bidders freely decide upon a pricethey want to bid. Strobel and Weinhardt (2003) presentan overview of possible parameters that might be used toreduce the complexity of bids. However, a completeenumeration of possible restrictions is impossible.

To enforce certain properties of the message flowwithin the mechanism, additional rules are often addedto a negotiation scheme. The following list gives typicalexamples of rules that further limit the bidders’ choices.

N Some auctions utilize the characteristics of sessionsand rounds. In this case a rule based bidding policy isused (round-based rules). Often this is used to reducestrategic behaviour, e.g. by forcing a participant tobid or get out of the auction.

N Rules that restrict a new bid based on previouslyplaced bids are called dominance rules. Generally,there are two kinds of such rules: ascending rulesrequire that new bids are higher while descendingrules require new bids to be lower than previous bids.This statement increases in complexity when quan-tity-price tuples or multi-dimensional auctions(including combinatorial auctions) are considered.The bid that is replaced could be the highest actualbid (English auction) or the bidder’s active bid.Moreover, such rules allow the enforcement ofminimal bid increments (or decrements) in interactivepricing schemes.

N While dominance rules refer to previous bids, someauction schemes require a bidder to increase the priceabove a certain minimum (possibly composed ofatomic item prices). Following Wurman et al. (2002)a classification named beat-the-quote is introduced torepresent such a condition. In the context of multi-dimensional auctions the calculation of atomic pricessurpasses the optimal allocation problem in terms of



complexity, i.e. it could be impossible. To overcomethis obstacle, shadow prices of the underlyingproblem are often used as price quotes for the bids’components and provide an indication of the pricesthe bidders have to beat (Stockheim et al. 2006a).

N Activity rules allow the reduction of strategic biddingin multi-item auctions. Activity rules describe rulesdefining when a bidder can be active and submit newbids or change bids. The rules can be used by theauctioneer to control strategic bidding behaviour(Kwasnica et al. 2005). They are applicable to mostkinds of interactive pricing mechanisms.

N A similar strategic behaviour could be systematicoverbidding and withdrawal of bids. To avoid suchpatterns withdrawal rules can be used. An alternativeto withdrawal is scheduled expiration of bids. Thetime of expiration typically has to be specified at bidtime. This category comprises indicative offers likethose mentioned in the ‘Montreal Taxonomy’(Strobel and Weinhardt 2003).

Clearing policy

The clearing policy describes when and how an exchangeof goods, services and money is initialized. The dealsresulting from the clearing process are determined bythe matching function. Immediately after the clearing nomutually profitable exchanges should be possible amongthe remaining bids. Moreover, it is often required thatthe allocation be locally efficient, meaning that itmaximizes welfare as represented by the bids. Thematching function depends on various external factorsand is closely related to the bidding rules of thenegotiation. Focusing on the observable properties ofthese mechanisms, we propose the following categoriza-tion of this category:

N Deals resulting from matching are coupled to prices.The simplest form of pricing in an auction is pay-as-you-bid, i.e. in an exchange the buyer pays the amountof money specified in the winning bid. Other forms ofpricing like second-price are also commonly applied.This kind of pricing belongs to the individual pricingcategory, where identical items can have differentprices (even to the same bidder). Whereas if items orbundles with identical properties have identical prices,this pricing is called anonymous pricing. For such casesquantity discounts are still feasible. Finally, linearpricing represents a simpler type with prices calculatedfor atomic items. With this attribute set each item issold at the same price.

N Matching functions may include side payments toincrease bidders’ incentive to express their W2P, todecrease the bidders’ incentive to cheat, to formcoalitions or to implement some idea of ‘fairness’ inthe mechanism. The calculation of side payments is

usually communicated to participants prior to theactual negotiations. Clarke Tax is a prominentexample of such a mechanism (Clarke 1971). Dueto the possible variety of rules for side payments theyare depicted as a category in Figure 2.

N Tie breaking may occur when different allocationswhich result in the same (cumulated) outcome for theseller/buyer or more general in the same allocationquality are possible. Three common methods ofsolving such ties in one-dimensional auctions are:breaking the goods arbitrarily, favouring earlier bids(first come first served) or favouring bids for largerquantities. In multi-dimensional settings the problemrarely occurs (then the first solution is typically used –implicitly at random).

The clear timing defines the schedule of time pointswhen the matching function is executed. A simple butunusual possibility is random clearing, meaning that thematching function is arbitrarily executed. Traditionalauction mechanisms mostly trigger the clearing mechan-ism only once as the last step of the mechanism (onclosing). A third possibility is activity-based triggers, e.g.whenever an agent makes a bid, the clearing ofmechanisms that finish when no agent increases its bidwithin a given interval of time is classified by the cleartiming attribute ‘on closing’. The closing condition is usedto end the auction depending on the activities of thebidders or a pre-defined timing. In many cases theclosing condition also triggers the clearing mechanism.For example, the well-known eBay auction would thusbe classified as on closing with a fixed time as closingcondition. More possible closing conditions follow inthe next paragraph. Frequently used timing options arethe inactivity-based trigger that clears the negotiationwhen no agent changes its bid and the fixed-time-pointstrigger, which invokes the clearing process repetitively(e.g. every minute). Combinations of the clearing timeattributes are possible.

To classify a trading mechanism it is necessary tospecify the event or point in time that terminates thenegotiation. This trigger is called the closing condition. Ifa trading mechanism runs constantly with no predefinedtemporal ending, the closing condition is never.However, frequently used triggers close at a fixed-timeor fixed-round or on inactivity of the bidders. Fornegotiations that try to reach an equilibrium price(defined by equal supply and demand at the given prices)the corresponding condition is on-match. This categoryalso specifies a closing that appears when a specific,externally given condition matches, e.g. a bid surpasses athreshold price.

The last part of the category clearing policy is theauctioneer fees. Although such fees are important to theuser of an auction, they do not necessarily classify anauction mechanism. Nevertheless, fees that are intendedto change the consumers’ bidding behaviour constitute a



part of the mechanism and should be considered in theclassification. It is even possible that payments are madein the opposite direction, e.g. to unsuccessful bidders toachieve certain incentives. To cover such cases, auction-eer fees are provided in the classification. Figure 3 depictsthree standard types of auctioneer fees: bid-, transaction-and entrance-fees. Splitting rules can be used todistribute fees between different parties, e.g. the sellerand the auctioneer.

Information policy

Information policy defines the kind of informationrevealed to bidders. The information provided couldinfluence the bidding behaviour and thus have a crucialimpact on the outcome of the pricing process.

N According to Strobel and Weinhardt (2003) electro-nic negotiation is anonymous if an assignment ofparticipants to their offers is not possible and theiridentities are not revealed. Information regarding theidentity of agents or the human/company the agentrepresents belongs to this category of information.

N A price quote informs a bidder of the price range ofthe accepted offers if the auction clears at the time thequote is issued. In other words, the price quote is ahypothetical clearing. An accurate price quote is notalways available. In complex auction settings such ascombinatorial auctions, the computation of theoptimal allocation requires considerable amounts oftime. Furthermore, it is not always possible tocalculate accurate prices (Stockheim et al. 2006a).

N The quote timing can have properties identical tothose of the clear timing. Additionally, in someauctions, especially online auctions, the actual quotesare always available (‘on request’).

N An order book shows the currently active bids. Theauctioneer can make all bids available or choose todisclose information selectively. The usual choices areall or nothing, i.e. either publishing the completeorder book or no information on previous bids at all.

N Most iterative negotiation or auction mechanismspublish historical clearing prices. These transactionhistories may include prices, quantities or even theidentities of the successful bidders.

N Various forms of incentive mechanisms constituteprimarily information regarding the mechanism char-acteristics. Besides legally necessary information aboutfees and side payments, incentives may be increasedby bonus systems. However, mechanism propertiesare not restricted to financial effects – the collection ofstatus-related bonus points plays an increasinglyimportant role in on- and offline mechanisms.

APPLICATION OF THE MODEL

The capabilities of our model will be demonstrated inthe following section by explaining its functionality withthree very different cases of interactive pricing: Thedescription of a reverse pricing scenario in a B2Cenvironment, the very prominent case of the GoogleAdWords-auction and a combinatorial auction fortransportation logistics. Table 3 displays the propertiesof these mechanisms:

Case 1: Reverse pricing

Reverse pricing was first established by Priceline.com in1998. Besides Priceline.com, several other companieslike Expedia.com and Germanwings apply this new

Figure 3. Windows application (left) transferring pricing data through web service to a marketplace (right)



mechanism using different design variants. Pricelineallows for a single bid within seven days for one andthe same product only, whereas bidders are allowed tobid repeatedly and increase their initial bid price if it hasnot surpassed the seller’s threshold in a multiple biddingcase. Such a mechanism has recently been introduced byeBay where buyers can place up to three offers which aseller may accept or reject (see http://pages.ebay.com/bestoffer for more information). In order to reduceincremental bidding behaviour when bidders – startingat the lowest bid – increment their bids by minimumincrements only, Bernhardt (2004) suggests the use ofcost constraints (e.g. a fee for the option to placeadditional bids) or time constraints (e.g. a waiting-timebetween two consecutive bids), many of which havebeen successfully applied in practice.

Combining the descriptive approach of Bernhardt(2004) and the previously proposed model, we derivethe following matching: First, reverse pricing is anegotiation between one seller and one buyer.Therefore, the cardinality is one-to-one. Bidding is donesolely by stating a bid for a product and bids can beplaced under certain constraints, e.g. waiting time,bidding costs. These variables are part of the expressive-ness defining the bidding language. Additional rules arenot necessary; however, a reasonable policy might be torequire new bids always to beat the bidder’s own bidsthat have been previously rejected.

In the case of reverse pricing, the clearing policy iscomposed of the following rules: Pricing is done on anindividual level and the process ends when a fixednumber of rounds (5 number of bids possible) isreached or when a certain condition is fulfilled (on-match): in this case, when the bid surpasses the secret

threshold. The clearing is invoked on closing. It is alsopossible to use the auctioneer fees to settle the biddingcosts generated and split them up using a splitting policyspecified beforehand. Finally, the information policyconsists of the rule that the price quote equals thebuyer’s bid (5 pay-as-you-bid) and this quote is‘calculated’ on-closing. An order book is irrelevant in thecase of reverse pricing, whereas the transaction historyconsists of the bidder’s previously rejected bids.Identities and price quotes are never published sincereverse pricing is based on intransparency.

The classification depicted in Figure 3 can be easilytranslated into XML. As proof-of-concept, we imple-mented a Web Service allowing the automated insertionof new offers using the specified XML dialect on anexisting reverse pricing platform.

Figure 3 illustrates a Windows application that invokesthis Web Service which sends the XML documentcontaining product data and the interactive pricinginformation based on our model. The Web Servicedecomposes these two parts and creates an offer which isdisplayed on the offer list. As can be seen in the right sideof Figure 3, the product is then ready to be purchased bybidders who place bids in conformance with theconstraints defined by the XML document on themarketplace. The ellipse marks the bid constraints inthe XML document and the corresponding informationthat is presented to bidders on the marketplace.

Case 2: Google AdWords-auction

Search engine marketing (SEM) with expenditures of710 million J in Germany in 2006 is one of the fastest

Table 3. Attributes of the combinatorial auction and reverse pricing within the IA design space

Reverse pricing Combinatorial auction Google AdWords

Participation policy Buyer / Seller one-to-one many-to-many one-to-many

Intermediary imperative imperative imperative

Bidding policy Expressiveness bid constraints combinatorial, bid

constraints

bid constraints

Additional rules none/dominance rule none none

Clearing policy Closing condition round or on-match fixed-time never

Clear timing on-closing on-closing activity

Auctioneer fees none/transaction fees none none

Matching Pricing pay-as-you-bid pay-as-you-bid second price

Tie-breaking none random ?

Side payments none none none

Information policy Identities unrevealed revealed unrevealed

Price quotes none pay-as-you-bid none

Quote timing on-closing on-closing never

Order book none none none

Transaction history yes none none

Mechanism characteristics yes none none



growing services in online marketing. SEM is also calledpaid search and works as follows: A consumer types akeyword, e.g. ‘laptop’, into a search engine and receivestwo types of results: One part with unsponsored searchresults which a search algorithm determines and thesecond part with sponsored search results. Clicking onone of the sponsored search results leads the consumerto the advertising company’s landing page, whichprovides further information and an opportunity tobuy or register. At the same time the search enginecharges the advertiser for the click. The price per click isa result of a keyword auction, by which search engineproviders sell their advertising space. The bids in suchauctions determine not only the price per click but alsothe rank (or position) of the ads in the sponsored linksarea (Kitts and Leblanc 2004; Varian 2006). Higher bidsresult in more attractive positions in the sponsored linksarea. However, the position of the ad in the sponsoredlinks area is also determined by so-called quality scores.The specific calculations for such scores are kept secret,but are essentially determined by the keyword’s histor-ical click through rate. Additionally, Google uses somestochastic elements to change the position of the addynamically making it hard to evaluate precisely theinfluence of the price on the final position of the ad.

However, we focus on the Google AdWords-auctionand not on the exact algorithm for the position. Theauction is an auction with one seller (Google) selling adspaces to many buyers (advertising companies), so thecardinality is one-to-many. Google is selling as well asconducting the auction, and so the results of the auctionare clearly imperative.

There are some bid constraints in Google’s biddingpolicy, e.g. there is a minimum price. The auction is neverclosing and winners are dynamically determined afteractivity, i.e. a new bid has been received. The price of thesecond winner determines the price that has to been paidfor a click-through by the winner, so the pricing is set tosecond price. The rules for tie-breaking have not beenpublicly reported yet and are hard to determine due to thestochastic element in the positioning.

Google pursues a very restrictive strategy in terms ofinformation: Identities of the bidders stay unrevealed andprice quotes are never published, though the identitiesof other bidders can be the subject of speculations.

We use the case of the Google AdWords-auction todemonstrate how our framework can be used to structurea prominent case of interactive pricing. However, we donot claim that Google used our framework. Cases 1 and 3though made use of our concept and developed a detailedXML dialect based on the framework presented.

Case 3: Combinatorial auctions

Combinatorial auctions (CAs) allow bidders to formu-late bids that comprise combinations of services or

goods. Bidders can express their W2P for each bundleby placing a bid on an available combination of singleitems. The valuation of the bundle can be subadditive,indicating that the bidder’s valuation for the bundleitems is lower than the sum of the valuation of thesingle items in the bundle. In contrast, a superadditivevaluation describes a bundle valuation that is higherthan the sum of the valuation of the items in thebundle (de Vries and Vohra 2001). The return-maximizing combinatorial allocation process performedby the auctioneer, the bidders’ ability to formulatebundles and the nonlinear valuation of the bids allconstitute the increased efficiency of the CA (Cramptonet al. 2006).

The use of CAs is an especially interesting method fordynamic pricing, if a combination of goods and servicesis a necessary input factor for the value creation process.This is the case in domains like supply chain manage-ment scheduling, procurement or logistics (Schwind2005). One application of CAs in the sector of logisticsis to use them for the exchange of transportationcapacities while taking the synergies that are generatedby the appropriate allocation of bundles of transporta-tion tasks to different carriers into consideration (Gujo etal. 2007, Gujo and Schwind 2007).

Figure 4 shows the GUI of a Web-based system forthe combinatorial auctioning of transportation routesbetween logistic service providers in an intra-enterpriseexchange of logistic services in an enterprise that isrelated to the food sector and organized in a profitcentre structure. In the intra-enterprise exchangeprocess, each profit centre can release delivery contractsfor outsourcing if the geographic location of a consumerallows a reduced-cost delivery by another profit centre inthe neighbourhood. The cost calculation is based on theresults of an integrated routing system, while the in- andout-sourcing process is managed by using the auctionmechanism ComEx (Gujo and Schwind 2007). In thefollowing we describe the characteristics of the under-lying auction process according to the interactive pricingscheme presented in Figure 2.

According to Figure 2 the first point in the negotia-tion model describes the participation policy by specify-ing the number of buyers/sellers involved in the auctionprocess. It determines the type of the CA by discrimi-nating between procurement or seller auctions and acombinatorial exchange as used in ComEx. Our applica-tion case has a many-to-many cardinality due to thebilateral role of the profit centres operating both assellers of delivery contracts in the out-sourcing case andbuyers of transportation tasks in the in-sourcing case.The intermediary is imperative in the ComEx settingbecause of its role as an auctioneer. The second maincategory of the scheme in Figure 2 is the biddingpolicy and includes the branch expressiveness, which inour case is combinatorial. The combinatorial property isrequired to enable the ComEx system to give the bidders



the opportunity to submit and bid for alternativecombinations of delivery contracts or tasks that aregeographically close to each other. The bidding processis coupled with the route planning process in the ComExsystem and uses an exclusive-or (XOR) notation for thecombinatorial bids. An important complement to thebidding rules are constraints on the formulation ofthe bids, e.g. the maximum transportation capacity thatcan be attributed to a single bidder. The bidding rulescan also be used to impose constraints on the allocationprocess. The clearing policy is another major issue in thedefinition of the combinatorial transportation servicesexchange. Closing condition is set to a fixed time in thiscase, due to the fact that the auction is a single roundauction. The tag clear timing denotes the beginning of atransaction and comes together with the on-closing signalof the ComEx auction. The tag marks the end of the bidacceptance phase and the beginning of the winnerdetermination process performed by the auctioneer. Thematching function then describes the winner determina-tion process used by the auctioneer and is coupled to thepricing function determining the amount of money thatfinally has to be spent for the intra-enterprise transporta-tion service in the ComEx system. In our logisticsscenario the price quotes are a pay-as-you-bid function.The introduction of side payments between bidders isuseful for incentive compatibility reasons (Gujo et al.2007) but not implemented in the current version ofComEx. In the case of two bids with the same W2P foridentical services, a tie-breaking-rule has to be intro-duced to decide which of the two bids wins. A verysimple way to resolve the tie-breaking problem is torandomly select a winner in such a case. The tie-breaking-rule can be set to random, because equality ofW2P is very unlikely while using combinatorial bids andthe overall result of the CA will not be perceptiblyaffected by this decision rule. Another important issuewhile using auctions is how information feedbackprovided to the bidders by the auctioneer is covered by

the information policy. ComEx only provides informationabout the acceptance of a bid at the end of the auction.The revelation of the bid prices, called quote timing isdone on-closing after the winner determination processhas taken place. Neither order book nor transaction-history exist in the ComEx system. Auctioneer fees are notcharged in the current version of ComEx and the identityof the bidders is revealed due to its intra-enterprisecharacter.

CONCLUSIONS

In our survey of common e-Business standards we revealthat they do not have the capability to support newforms of pricing mechanisms. While the possibilities ofdynamic posted pricing are foreseen, interactive pricingfunctionality is not yet supported by existing e-Businessstandards. This confirms the findings of a large-scalesurvey among German companies conducted bySackmann and Struker (2005). The lack of standardiza-tion might be an obstacle to the broader application ofinteractive pricing which can increase allocation effi-ciency and thus welfare. As yet, business has notexploited the full potential of price differentiation,although many studies have shown that interactivepricing is well accepted among both consumers andbusiness partners.

For the sake of unification we therefore present auniversal model that allows the explicit description of abroad range of interactive pricing mechanismswith respect to their informational and economicrequirements. The model can easily be translated intoXML and might thus be a guideline for industrialstandardization efforts. As all e-Business standards havetheir own nature, we do not elaborate the XMLstructure in detail. However, as proof of concept, weillustrate the application of our model for two differentcases of interactive pricing scenarios.

Figure 4. GUI of a web-based system for the combinatorial auctioning of transportation routes between logistic service providers



The resulting XML structure can be used to describeinteractive pricing mechanisms, store them for later useor can be handed to an intermediary such as anelectronic marketplace to conduct the pricing processin the way described. Since many sellers offer theirproducts on different sales channels like eBay,Overstock.com and their own online shop to targetdifferent market segments, such a standard could reduceprocessing costs dramatically.

However, our approach would additionally profitfrom the following extensions: Dynamics could beintroduced in the generation of our XML language toenable it to respond to changing negotiation situationsduring the interactive pricing process. Our approach canhandle a large number of language specifications. Thus aversioning system and sub-classes for the particularinteractive pricing methods could be introduced intoour model. Nevertheless, our model represents an initialapproach to closing the gap between models developedin academia and e-Business standards that are frequentlyused in business practice. We do this by focusing on theendogenous variables that can be actively changed by theparty initiating the price finding process. Following ourcall for standardization in the domain of interactivepricing mechanisms, a larger number of applicationclasses will enable users of interactive pricing methods toeasily create new variants of pricing. We expect differentstandardization approaches in the near future which willsolve individual problems in the area of interactivepricing, but encourage a broad solution for this domain.This could significantly drive the development of e-Commerce and e-Business and lead to a businessenvironment connected even more closely online.

ACKNOWLEDGEMENTS

This paper originates from research conducted withinthe framework of the ‘Internetokonomie’ project fundedby the German Federal Ministry of Science andEducation (BMBF). We would like to thank theparticipating organizations, consortia members, partnersand our dear colleagues for their support.

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