E-loyalty networks in online auctions

Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers can ``switch'' to a competitor with the click of a mouse. In this paper we investigate e-loyalty in online auctions. Using a unique data set of over 30,000 auctions from one of the main consumer-to-consumer online auction houses, we propose a novel measure of e-loyalty via the associated network of transactions between bidders and sellers. Using a bipartite network of bidder and seller nodes, two nodes are linked when a bidder purchases from a seller and the number of repeat-purchases determines the strength of that link. We employ ideas from functional principal component analysis to derive, from this network, the loyalty distribution which measures the perceived loyalty of every individual seller, and associated loyalty scores which summarize this distribution in a parsimonious way. We then investigate the effect of loyalty on the outcome of an auction. In doing so, we are confronted with several statistical challenges in that standard statistical models lead to a misrepresentation of the data and a violation of the model assumptions. The reason is that loyalty networks result in an extreme clustering of the data, with few high-volume sellers accounting for most of the individual transactions. We investigate several remedies to the clustering problem and conclude that loyalty networks consist of very distinct segments that can best be understood individually.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS310 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Analisis Welfare Loss Konsumen Sambungan Langsung Jarak Jauh Telepon Tetap Di Indonesia

Telendensitas telepon tetap di Indonesia dipengaruhi oleh struktur pasar industri telepon tetap di Indonesia. Pasar telepon tetap Indonesia merupakan pasar monopoli yang didominasi oleh Telkom. Telkom sebagai market leader mempunyai kebijakan kenaikan tarif Sambungan Langsung Jarak Jauh (SLJJ) telepon tetap yang akan berdampak peningkatan welfare loss kosumen. Penelitian ini bertujuan untuk mengetahui tingkat kemahalan (overprice) tarif SLJJ dan tingkat welfare loss kosumen Telkom di Indonesia. Penelitian ini menggunakan data sekunder. Metode analisis penelitian ini menggunakan sintesis perhitungan welfare cost dengan compensating variation dan equivalent variation yang diintegrasikan dalam cobb douglas models. Hasil penelitian menunjukkan kebijakan kenaikan tarif SLJJ Telkom Indonesia pada periode 2002-2007 telah menciptakan welfare loss konsumen yang semakin tinggi. Kebijakan kenaikan tarif SLJJ yang diterapkan Telkom Indonesia mempunyai overprice pada level 10 %, 25 % dan 75 % yang berimplikasi pada tingkat kemahalan tarif. Kenaikan tarif SLJJ akan menguntungkan perusahaan monopoli namun konsumen SLJJ di Indonesia akan mengalami penurunan kesejahteraan terhadap pemberlakukan tarif tersebut. Kebijakan tersebut bertolak belakang dengan tujuan pemerintah untuk menyejahterakan seluruh masyarakat dengan tarif SLJJ yang terjangkau

Stochastic Modelling and Optimisation of Internet Auction Processes

AbstractInternet auctions are an attractive mechanism for the exchange of goods at a non-fixed price point. The operation of these auctions can be run under a variety of parameters. In this paper, we provide a theoretical analysis of fixed time forward auctions in cases where a single bid or multiple bids are accepted in a single auction. A comparison of the economic benefits and the corresponding buyer and seller surpluses between the auctions where a single bid is accepted and the auctions where multiple bids are accepted is made. These models are verified through systematic simulation experiments, based on a series of operational assumptions, which characterise the arrival rate of bids, as well as the distribution from which the private values of buyers are sampled. Decision rules for optimising surplus under different auction fee structures are also given

The Happiness Premium: The Impact of Emotion on Individuals’ Willingness to Pay in Online Auctions

Much research across various disciplines has studied individuals’ bidding behavior in online auctions. Emotion is an important factor affecting individual behavior, but we know little about its effects in online auctions. We conducted a lab experiment to investigate the impact of positive emotion on individuals’ willingness to pay in online auctions. We found that individuals with positive emotions bid more than those with neutral emotions; that is, they paid a “happiness premium” of about 10 percent. The effect size was medium (Cohen’s d = 0.51). This study contributes to electronic commerce literature by identifying emotion as an important factor affecting online auction behavior. The findings also provide guidance to auction website design: websites can increase bid amounts by inducing positive emotions in potential customers

A USER’S COGNITIVE WORKLOAD PERSPECTIVE IN NEGOTIATION SUPPORT SYSTEMS: AN EYE-TRACKING EXPERIMENT

Replying to several research calls, I report promising results from an initial experiment which com-pares different negotiation support system approaches concerning their potential to reduce a user’s cognitive workload. Using a novel laboratory-based non-intrusive objective measurement technique which derives the user’s cognitive workload from pupillary responses and eye-movements, I experi-mentally evaluated a standard, a chat-based, and an argumentation-based negotiation support system and found that a higher assistance level of negotiation support systems actually leads to a lower user’s cognitive workload. In more detail, I found that an argumentation-based system which fully automates the generation of the user’s arguments significantly decreases the user’s cognitive workload compared to a standard system. In addition I found that a negotiation support system implementing an additional chat function significantly causes higher cognitive workload for users compared to a standard system

Estimating consumer surplus in ebay computer monitor auctions

Cataloged from PDF version of article.In this study, using data from computer monitor auctions on eBay collected in 2000, bidding functions are estimated by maximum likelihood using five different assumptions about the underlying distribution of independent private values. It is assumed that these values come from the logistic, gamma, weibull, pareto and lognormal distributions. Next, consumer surplus in the market for computer monitors is estimated and its sensitivity to different distributional specifications is tested. Two types of consumer surplus estimates are provided. First, ex-post consumer surplus estimates are constructed and then a lower bound for consumer surplus is computed using a “rational reassignment” methodology. Median consumer surplus estimates vary from $39 with the logistic to$143 with the lognormal, or the consumers’ share of surplus from 30% to 61%. Expected consumer surplus estimates indicate a high sensitivity to the distribution specification, especially to the tails of the distribution. Lower bound estimates are more solid and more reliable since they are independent of the tails. Accordingly, these statistics, which do not vary with distribution, yield a median estimate of $41 and a consumer share of 32%. Finally, the last part of this study examines which distribution best fits the data. Information criteria favor the gamma distribution, tests against the empirical distribution of second and third highest values prefer the logistic distribution.Giray, TuğbaM.S

Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact

Information systems and the Internet have facilitated the creation of used-product markets that feature a dramatically wider selection, lower search costs, and lower prices than their brick-and-mortar counterparts do. The increased viability of these used-product markets has caused concern among content creators and distributors, notably the Association of American Publishers and Author’s Guild, who believe that used-product markets will significantly cannibalize new product sales. This proposition, while theoretically possible, is based on speculation as opposed to empirical evidence. In this paper, we empirically analyze the degree to which used products cannibalize new-product sales for books—one of the most prominent used-product categories sold online. To do this, we use a unique data set collected from Amazon.com’s new and used book marketplaces to measure the degree to which used products cannibalize new-product sales. We then use these estimates to measure the resulting first-order changes in publisher welfare and consumer surplus. Our analysis suggests that used books are poor substitutes for new books for most of Amazon’s customers. The cross-price elasticity of new-book demand with respect to used-book prices is only 0.088. As a result, only 16% of used-book sales at Amazon cannibalize new-book purchases. The remaining 84% of used-book sales apparently would not have occurred at Amazon’s new-book prices. Further, our estimates suggest that this increase in book readership from Amazon’s used-book marketplace increases consumer surplus by approximately $67.21 million annually. This increase in consumer surplus, together with an estimated$45.05 million loss in publisher welfare and a $65.76 million increase in Amazon’s profits, leads to an increase in total welfare to society of approximately$87.92 million annually from the introduction of used-book markets at Amazon.com.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc