7,275 research outputs found
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
The BARISTA: A model for bid arrivals in online auctions
The arrival process of bidders and bids in online auctions is important for
studying and modeling supply and demand in the online marketplace. A popular
assumption in the online auction literature is that a Poisson bidder arrival
process is a reasonable approximation. This approximation underlies theoretical
derivations, statistical models and simulations used in field studies. However,
when it comes to the bid arrivals, empirical research has shown that the
process is far from Poisson, with early bidding and last-moment bids taking
place. An additional feature that has been reported by various authors is an
apparent self-similarity in the bid arrival process. Despite the wide evidence
for the changing bidding intensities and the self-similarity, there has been no
rigorous attempt at developing a model that adequately approximates bid
arrivals and accounts for these features. The goal of this paper is to
introduce a family of distributions that well-approximate the bid time
distribution in hard-close auctions. We call this the BARISTA process (Bid
ARrivals In STAges) because of its ability to generate different intensities at
different stages. We describe the properties of this model, show how to
simulate bid arrivals from it, and how to use it for estimation and inference.
We illustrate its power and usefulness by fitting simulated and real data from
eBay.com. Finally, we show how a Poisson bidder arrival process relates to a
BARISTA bid arrival process.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS117 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
eBay users form stable groups of common interest
Market segmentation of an online auction site is studied by analyzing the
users' bidding behavior. The distribution of user activity is investigated and
a network of bidders connected by common interest in individual articles is
constructed. The network's cluster structure corresponds to the main user
groups according to common interest, exhibiting hierarchy and overlap. Key
feature of the analysis is its independence of any similarity measure between
the articles offered on eBay, as such a measure would only introduce bias in
the analysis. Results are compared to null models based on random networks and
clusters are validated and interpreted using the taxonomic classifications of
eBay categories. We find clear-cut and coherent interest profiles for the
bidders in each cluster. The interest profiles of bidder groups are compared to
the classification of articles actually bought by these users during the time
span 6-9 months after the initial grouping. The interest profiles discovered
remain stable, indicating typical interest profiles in society. Our results
show how network theory can be applied successfully to problems of market
segmentation and sociological milieu studies with sparse, high dimensional
data.Comment: Major revision of the manuscript. Methodological improvements and
inclusion of analysis of temporal development of user interests. 19 pages, 12
figures, 5 table
Modeling On-Line Art Auction Dynamics Using Functional Data Analysis
In this paper, we examine the price dynamics of on-line art auctions of
modern Indian art using functional data analysis. The purpose here is not just
to understand what determines the final prices of art objects, but also the
price movement during the entire auction. We identify several factors, such as
artist characteristics (established or emerging artist; prior sales history),
art characteristics (size; painting medium--canvas or paper), competition
characteristics (current number of bidders; current number of bids) and auction
design characteristics (opening bid; position of the lot in the auction), that
explain the dynamics of price movement in an on-line art auction. We find that
the effects on price vary over the duration of the auction, with some of these
effects being stronger at the beginning of the auction (such as the opening bid
and historical prices realized). In some cases, the rate of change in prices
(velocity) increases at the end of the auction (for canvas paintings and
paintings by established artists). Our analysis suggests that the opening bid
is positively related to on-line auction price levels of art at the beginning
of the auction, but its effect declines toward the end of the auction. The
order in which the lots appear in an art auction is negatively related to the
current price level, with this relationship decreasing toward the end of the
auction. This implies that lots that appear earlier have higher current prices
during the early part of the auction, but that effect diminishes by the end of
the auction. Established artists show a positive relationship with the price
level at the beginning of the auction. Reputation or popularity of the artists
and their investment potential as assessed by previous history of sales are
positively related to the price levels at the beginning of the auction. The
medium (canvas or paper) of the painting does not show any relationship with
art auction price levels, but the size of the painting is negatively related to
the current price during the early part of the auction. Important implications
for auction design are drawn from the analysis.Comment: Published at http://dx.doi.org/10.1214/088342306000000196 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Trust and Experience in Online Auctions
This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust
Rate of Price Discovery in Iterative Combinatorial Auctions
We study a class of iterative combinatorial auctions which can be viewed as
subgradient descent methods for the problem of pricing bundles to balance
supply and demand. We provide concrete convergence rates for auctions in this
class, bounding the number of auction rounds needed to reach clearing prices.
Our analysis allows for a variety of pricing schemes, including item, bundle,
and polynomial pricing, and the respective convergence rates confirm that more
expressive pricing schemes come at the cost of slower convergence. We consider
two models of bidder behavior. In the first model, bidders behave
stochastically according to a random utility model, which includes standard
best-response bidding as a special case. In the second model, bidders behave
arbitrarily (even adversarially), and meaningful convergence relies on properly
designed activity rules
Online Auctions
The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).
The Timing of Bid Placement and Extent of Multiple Bidding: An Empirical Investigation Using eBay Online Auctions
Online auctions are fast gaining popularity in today's electronic commerce.
Relative to offline auctions, there is a greater degree of multiple bidding and
late bidding in online auctions, an empirical finding by some recent research.
These two behaviors (multiple bidding and late bidding) are of ``strategic''
importance to online auctions and hence important to investigate. In this
article we empirically measure the distribution of bid timings and the extent
of multiple bidding in a large set of online auctions, using bidder experience
as a mediating variable. We use data from the popular auction site
\url{www.eBay.com} to investigate more than 10,000 auctions from 15 consumer
product categories. We estimate the distribution of late bidding and multiple
bidding, which allows us to place these product categories along a continuum of
these metrics (the extent of late bidding and the extent of multiple bidding).
Interestingly, the results of the analysis distinguish most of the product
categories from one another with respect to these metrics, implying that
product categories, after controlling for bidder experience, differ in the
extent of multiple bidding and late bidding observed in them. We also find a
nonmonotonic impact of bidder experience on the timing of bid placements.
Experienced bidders are ``more'' active either toward the close of auction or
toward the start of auction. The impact of experience on the extent of multiple
bidding, though, is monotonic across the auction interval; more experienced
bidders tend to indulge ``less'' in multiple bidding.Comment: Published at http://dx.doi.org/10.1214/088342306000000123 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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