This paper investigates the impact of seller introduced inference systems designed to modify buyer behavior on an electronic market. In particular, the focus of our analysis is on real time systems that seek to infer buyer preferences and reservation prices based on buyer behavior. We show that under certain conditions, when an inference engine is introduced, the equilibrium price falls and the total surplus of the market increases. We will employ the technique of closed form equations to model these phenomena and interpret the solutions and relate the results to current trends in electronic commerce

Arunkundram, Ravi

Kambil, Ajit

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 1998 Proceedings Americas Conference on Information Systems(AMCIS)December 1998The Impact of Interactive Behavior Modification onEquilibrium Price and Seller Location in ElectronicMarketsRavi ArunkundramNew York UniversityAjit KambilNew York UniversityFollow this and additional works at: http://aisel.aisnet.org/amcis1998This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 1998 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationArunkundram, Ravi and Kambil, Ajit, "The Impact of Interactive Behavior Modification on Equilibrium Price and Seller Location inElectronic Markets" (1998). AMCIS 1998 Proceedings. 92.http://aisel.aisnet.org/amcis1998/92-266-The Impact of Interactive Behavior Modification on Equilibrium Price andSeller Location in Electronic MarketsRavi ArunkundramAjit Kambil Information Systems DepartmentStern School of Business New York UniversityAbstractThis paper investigates the impact of seller introduced inference systems designed to modify buyer behavioron an electronic market. In particular, the focus of our analysis is on real time systems that seek to infer buyerpreferences and reservation prices based on buyer behavior.  We show that under certain conditions, whenan inference engine is introduced, the equilibrium price falls and the total surplus of the market increases. Wewill employ the technique of closed form equations to model these phenomena and interpret the solutions andrelate the results to current trends in electronic commerce. IntroductionThe Internet has emerged as an effective forum for electronic commerce that integrates widely dispersed buyers and sellersand encourages them to transact by adopting both synchronous and asynchronous buying and selling activities. This in turn hasfostered the growth of Internet-based electronic marketplaces, which allow trading between geographically distributed buyersand sellers.  A major impact of such marketplaces is the resulting reduction in buyer search costs, as buyers can easily access,compare and evaluate product offerings from different sellers.  This reduction in search costs may promote "frictionless"competitive markets, resulting in increased market efficiency, but also lower prices and lower seller profits (e.g., see Bakos1987). While search engines and search mechanisms allow buyers to seek sellers almost costlessly, there are very  few symmetricmechanisms that allow sellers to locate buyers or become aware of buyer preferences for various product attributes. Sellers arebeginning to use non-intrusive methods of gathering information about buyers’ preferences, by using intelligent agents like"LifestyleFinder" (see Heidi Hilgens) which employ a technique called ’collaborative filtering’. The introduction of inferenceengines such as LifeStyleFinder marks the beginning of a trend where sellers attempt to gather information about buyerpreferences, demographic attributes and lifestyles that will in turn allow them to infer their product attribute preferences and areservation price for the product. Using intelligent agents to observe buyer behavior on the web and relate it to buyer preferencesis however, not enough to influence either buyer behavior or gain an understanding of buyer preferences with any degree ofaccuracy. Therefore, it is necessary to employ techniques that leverage the interactive power of the web and facilitate thecapturing of buyer preferences at a greater degree of granularity. This paper addresses the task of measuring the benefits of suchinteractive mechanisms and their impact on the equilibrium behavior of buyers and sellers. We do not propose a design of suchsystems or construct algorithms that can be used by the inference engines. Instead we seek to answer the following questions:• What is the value to sellers of such Inference engines?• What is the value to buyers of such inference mechanisms and what are  the resulting impacts on buyer and sellersurplus?A theoretical model that explains the motivation of buyers and sellers in  introducing these mechanisms will be constructedand through analytically solving closed form equations,  solutions for different scenarios will be provided.Inference Mechanisms Based on  Behavior ModificationThe different sources of inference available in the market today, can be analyzed along the two dimensions of  predictiveaccuracy and implementational difficulty. Fig 1, below compares the four broad categories of inference sources.The highest predictive accuracy is provided by real-time behavior modification systems, which capture buyer responses totest stimuli and analyze these against buyers’ purchase data. To obtain a significant degree of predictive accuracy, it is necessaryto induce buyers to participate in  structured, sequential, stimulus-response games which capture the buyers’ responses to stateof the world stimuli. Comparison of these responses against buyers’ purchase decisions in the past provides this model with itspredictive accuracy. To induce buyer participation, it will be necessary for the sellers as a group to offer incentives which carrycost implications for sellers. We will show through our model that sellers can actually gain by offering buyers such incentives.The next section describes the research model.-267-Predictive AccuracyImplementational DifficultySite DemographicsBuyer DemographicsCollaborative FilteringObserved Real-tine BehaviorResearch Model1. The market consists ofseller offerings and buyerpreferences that arespatially differentiated inan interval normalizedwithout loss of generalityto [0,1]. 2. Sellers are not aware of theexact distribution of buyersalong the dimension ofproduct differentiation andstart with the prior thatbuyers are uniformlydistributed along theproduct dimension. Sellersare therefore uniformlydistributed along theproduct dimension.3. At equilibrium each sellersells q units and his price isgiven by his average costfor q units. All sellerofferings are priced at P* =AC(q). 4. Buyer preferences are uniformly distributed in an interval normalized without loss of generality to be [0,1] (same intervalas seller offerings).  The unit "fit" cost of differentiation is t: a seller offering located at distance x from a buyer’s idealproduct, results in the buyer facing a "fit" cost of xt. When a buyer finds a  seller at a distance xi from his ideal productlocation who offers a product at price P* his disutility (from having to pay this price and from the mismatch between hisideal product and the seller’s offering)  is given by:D ’ P( % tx15. Buyers enter the market with a reservation utility R and if a seller offering fails to give them that utility they exit the marketwithout buying the product. They therefore search for all sellers at a distance xi from them such that:A buyer whose position is [ ,0] searches for seller   offerings within a distance x around himR $ P( % xi tgiven by:R $ P( % t× * &x*Which is a region given by:[ & R&P(t,0] and [ % R&P(t,0]Any seller offerings that lie outside this region will not meet the buyer’s reserve utility criterion and will not bebought. A buyer who is unable to locate an offering will exit the market without committing to the transaction. Werefer to this as an instance of market failure.6. An inference mechanism that analyzes buyers’ responses to interactive stimuli offers an important benefits to sellers; greateraccuracy in predicting buyer location in differentiated product space7. When buyers are not distributed uniformly in product space but are distributed according to a Normal distribution N( m,s),where m represents a highly significant product attribute value, sellers would not want to position themselves at equaldistances along the product space. They would instead assume positions along the product space that would maximize theirexpected revenue. We show by solving the closed form equations of our model that this is possible only if the re-organization of seller product positioning results in fewer cases of market failures (more buyers actually buy products). Thefollowing benefits accrue to buyers: • There are fewer buyers who exit the market without buying a product.• The total buyer disutility (summed across all buyers) arising from mismatch between buyers' ideal products and sellers'offering(s) decreases, thereby resulting in higher buyer surplus.Conclusions1. We show that seller awareness of buyers’ location in differentiated product space will lead to a higher total surplus in themarket, thereby establishing the distinct economic benefit associated with these mechanisms. 2. The model provides a basis for analyzing the impact of lower production costs that result when buyer preferences areprecisely known through inferences made by a system of  intelligent agents.-268-Directions for Future Research1. The determining of an economic value of behavior modification based inference will result in bringing greater clarity toassessing the value of interactive advertising advertisement on the Web and the associated questions of what is the bestmethod to measure  the value of the advertisement to the advertiser.2. Intelligent agents are to sellers what search engines are to buyers. Agent technology allows sellers to search for buyers andassess their product preferences. To predict buyer preferences accurately, agent technologies will have to address the issueof belief revision and devise algorithms that will allow them to update a set of priors based on the observed buyer responseto agent generated stimuli.3. To ensure that buyers do not have disincentives to reveal information to intelligent agents, the design of intelligent agentsmust incorporate algorithms that  conceal buyers’ reserve utilities from sellers. The construction of such algorithms thatensure parity in optimization of market surplus between buyers and sellers will yield important research issues.ReferencesBakos, J. Y.  "Interorganizational Information Systems: Strategic Opportunities for Competition and Cooperation."  Ph.D.Dissertation, MIT Sloan School of Management, November 1987.A more detailed list is available on request from the authors (rarukun@stern.nyu.edu; akambil@stern.nyu.edu).

The Impact of Interactive Behavior Modification on Equilibrium Price and Seller Location in Electronic Markets

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The Impact of Interactive Behavior Modification on Equilibrium Price and Seller Location in Electronic Markets

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