10 research outputs found
Used Good Trade Patterns: A Cross-Country Comparison of Electronic Secondary Markets
A series of recent papers have investigated the nature of trading and
sorting induced by the dynamic price mechanism in a competitive durable
good market with adverse selection and exogenous entry of traders over
time. These models are dynamic versions of Akerlof's (1970) seminal
work. The general set up consist of identical cohorts of durable goods,
whose quality is known only to potential sellers, enter the market over
time and a common result is that there exists a cyclical equilibrium
where all goods are traded within a finite number of periods after
entry. Market failure is reflected in the relationship between product
quality (and product reliability) and the length of waiting time before
trade as well as on the relationship between average price decline and
extent of trade of used goods. Based on a unique 9-month dataset
collected from Amazon's secondary market across multiple countries, and
multiple product categories we provide empirical evidence of trade
patterns and the presence of adverse selection. We show how used good
quality and product reliability affect resale turnaround times in an
electronic secondary market. We find some empirical evidence that is
consistent with theoretical predictions existing in the literature
An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets
The phenomenon of sponsored search advertising – where advertisers
pay a fee to Internet search engines to be displayed alongside organic
(non-sponsored) web search results – is gaining ground as the
largest source of revenues for search engines. Using a unique panel
dataset of several hundred keywords collected from a large nationwide
retailer that advertises on Google, we empirically model the
relationship between different metrics such as click-through rates,
conversion rates, bid prices and keyword ranks. Our paper proposes a
novel framework and data to better understand what drives these
differences. We use a Hierarchical Bayesian modeling framework and
estimate the model using Markov Chain Monte Carlo (MCMC) methods. We
empirically estimate the impact of keyword attributes on consumer search
and purchase behavior as well as on firms’ decision-making
behavior on bid prices and ranks. We find that the presence of
retailer-specific information in the keyword increases click-through
rates, and the presence of brand-specific information in the keyword
increases conversion rates. Our analysis provides some evidence that
advertisers are not bidding optimally with respect to maximizing the
profits. We also demonstrate that as suggested by anecdotal evidence,
search engines like Google factor in both the auction bid price as well
as prior click-through rates before allotting a final rank to an
advertisement. Finally, we conduct a detailed analysis with product
level variables to explore the extent of cross-selling opportunities
across different categories from a given keyword advertisement. We find
that there exists significant potential for cross-selling through search
keyword advertisements. Latency (the time it takes for consumer to place
a purchase order after clicking on the advertisement) and the presence
of a brand name in the keyword are associated with consumer spending on
product categories that are different from the one they were originally
searching for on the Internet
Versioning and Quality Distortion in Software? Evidence from E-CommercePanel Data
We present a framework for measuring software quality using pricing and
demand data, and empirical estimates that quantify the extent of quality
degradation associated with software ver- sioning. Using a 7-month,
108-product panel of software sales from Amazon.com, we document the
extent to which quality varies across different software versions,
estimating quality degradation that ranges from as little as 8% to as
much as 56% below that of the corresponding flagship ver- sion.
Consistent with prescriptions from the theory of vertical
di¤erentiation, we also find that an increase in the total number
of versions is associated with an increase in the difference in quality
between the highest and lowest quality versions, and a decrease in the
quality difference between 'neighboring' versions. We compare our
estimates with those derived from two sets of subjective measures of
quality, based on CNET editorial ratings and Amazon.com user reviews,
and discuss competing interpretations of the significant differences
that emerge from this comparison. As the first empirical study of
software versioning that is based on both subjective and econometrically
estimated measures of quality, this paper provides a framework for
testing a wide variety of results in IS that are based on related models
of vertical differentiation, and its findings have important
implications for studies that treat web-based user ratings as cardinal data
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
The increasing pervasiveness of the Internet has dramatically changed
the way that consumers shop for goods. Consumer-generated product
reviews have become a valuable source of information for customers, who
read the reviews and decide whether to buy the product based on the
information provided. In this paper, we use techniques that decompose
the reviews into segments that evaluate the individual characteristics
of a product (e.g., image quality and battery life for a digital
camera). Then, as a major contribution of this paper, we adapt methods
from the econometrics literature, specifically the hedonic regression
concept, to estimate: (a) the weight that customers place on each
individual product feature, (b) the implicit evaluation score that
customers assign to each feature, and (c) how these evaluations affect
the revenue for a given product. Towards this goal, we develop a novel
hybrid technique combining text mining and econometrics that models
consumer product reviews as elements in a tensor product of feature and
evaluation spaces. We then impute the quantitative impact of consumer
reviews on product demand as a linear functional from this tensor
product space. We demonstrate how to use a low-dimension approximation
of this functional to significantly reduce the number of model
parameters, while still providing good experimental results. We evaluate
our technique using a data set from Amazon.com consisting of sales data
and the related consumer reviews posted over a 15-month period for 242
products. Our experimental evaluation shows that we can extract
actionable business intelligence from the data and better understand the
customer preferences and actions. We also show that the textual portion
of the reviews can improve product sales prediction compared to a
baseline technique that simply relies on numeric data
A Dynamic Structural Model of User Learning in Mobile Media Content
Consumer adoption and usage of mobile communication and multimedia
content services has been growing steadily over the past few years in
many countries around the world. In this paper, we develop and estimate
a structural model of user behavior and learning with regard to content
generation and usage activities in mobile digital media environments.
Users learn about two different categories of content – content
from regular Internet social networking and community (SNC) sites and
that from mobile portal sites. Then they can choose to engage in the
creation (uploading) and consumption (downloading) of multi-media
content from these two categories of websites. In our context, users
have two sources of learning about content quality – (i) direct
experience through their own content creation and usage behavior and
(ii) indirect experience through word-of-mouth such as the content
creation and usage behavior of their social network neighbors. Our model
seeks to explicitly explain how direct and indirect experiences from
social interactions influence the content creation and usage behavior of
users over time. We estimate this model using a unique dataset of
consumers' mobile media content creation and usage behavior over a
3-month time period. Our estimates suggest that when it comes to user
learning from direct experience, the content that is downloaded from
mobile portals has the highest average quality level. In contrast,
content that is downloaded by users from SNC websites has the lowest
average quality level. Besides, the order of magnitude of accuracy of
signals for each content type from direct experiences is consistent with
the order of the quality levels. This finding implies that in the
context of mobile media users make content choices based on their
perception of differences in both the average content quality levels and
the extent of content quality variation. Further we find that signals
about the quality of content from direct experience are more accurate
than signals from indirect experiences. Potential implications for
mobile phone operators and advertisers are discussed
An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets
The phenomenon of sponsored search advertising – where advertisers
pay a fee to Internet search engines to be displayed alongside organic
(non-sponsored) web search results – is gaining ground as the
largest source of revenues for search engines. Using a unique panel
dataset of several hundred keywords collected from a large nationwide
retailer that advertises on Google, we empirically model the
relationship between different metrics such as click-through rates,
conversion rates, bid prices and keyword ranks. Our paper proposes a
novel framework and data to better understand what drives these
differences. We use a Hierarchical Bayesian modeling framework and
estimate the model using Markov Chain Monte Carlo (MCMC) methods. We
empirically estimate the impact of keyword attributes on consumer search
and purchase behavior as well as on firms’ decision-making
behavior on bid prices and ranks. We find that the presence of
retailer-specific information in the keyword increases click-through
rates, and the presence of brand-specific information in the keyword
increases conversion rates. Our analysis provides some evidence that
advertisers are not bidding optimally with respect to maximizing the
profits. We also demonstrate that as suggested by anecdotal evidence,
search engines like Google factor in both the auction bid price as well
as prior click-through rates before allotting a final rank to an
advertisement. Finally, we conduct a detailed analysis with product
level variables to explore the extent of cross-selling opportunities
across different categories from a given keyword advertisement. We find
that there exists significant potential for cross-selling through search
keyword advertisements. Latency (the time it takes for consumer to place
a purchase order after clicking on the advertisement) and the presence
of a brand name in the keyword are associated with consumer spending on
product categories that are different from the one they were originally
searching for on the Internet
A Dynamic Structural Model of User Learning in Mobile Media Content
Consumer adoption and usage of mobile communication and multimedia
content services has been growing steadily over the past few years in
many countries around the world. In this paper, we develop and estimate
a structural model of user behavior and learning with regard to content
generation and usage activities in mobile digital media environments.
Users learn about two different categories of content – content
from regular Internet social networking and community (SNC) sites and
that from mobile portal sites. Then they can choose to engage in the
creation (uploading) and consumption (downloading) of multi-media
content from these two categories of websites. In our context, users
have two sources of learning about content quality – (i) direct
experience through their own content creation and usage behavior and
(ii) indirect experience through word-of-mouth such as the content
creation and usage behavior of their social network neighbors. Our model
seeks to explicitly explain how direct and indirect experiences from
social interactions influence the content creation and usage behavior of
users over time. We estimate this model using a unique dataset of
consumers' mobile media content creation and usage behavior over a
3-month time period. Our estimates suggest that when it comes to user
learning from direct experience, the content that is downloaded from
mobile portals has the highest average quality level. In contrast,
content that is downloaded by users from SNC websites has the lowest
average quality level. Besides, the order of magnitude of accuracy of
signals for each content type from direct experiences is consistent with
the order of the quality levels. This finding implies that in the
context of mobile media users make content choices based on their
perception of differences in both the average content quality levels and
the extent of content quality variation. Further we find that signals
about the quality of content from direct experience are more accurate
than signals from indirect experiences. Potential implications for
mobile phone operators and advertisers are discussed
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
The increasing pervasiveness of the Internet has dramatically changed
the way that consumers shop for goods. Consumer-generated product
reviews have become a valuable source of information for customers, who
read the reviews and decide whether to buy the product based on the
information provided. In this paper, we use techniques that decompose
the reviews into segments that evaluate the individual characteristics
of a product (e.g., image quality and battery life for a digital
camera). Then, as a major contribution of this paper, we adapt methods
from the econometrics literature, specifically the hedonic regression
concept, to estimate: (a) the weight that customers place on each
individual product feature, (b) the implicit evaluation score that
customers assign to each feature, and (c) how these evaluations affect
the revenue for a given product. Towards this goal, we develop a novel
hybrid technique combining text mining and econometrics that models
consumer product reviews as elements in a tensor product of feature and
evaluation spaces. We then impute the quantitative impact of consumer
reviews on product demand as a linear functional from this tensor
product space. We demonstrate how to use a low-dimension approximation
of this functional to significantly reduce the number of model
parameters, while still providing good experimental results. We evaluate
our technique using a data set from Amazon.com consisting of sales data
and the related consumer reviews posted over a 15-month period for 242
products. Our experimental evaluation shows that we can extract
actionable business intelligence from the data and better understand the
customer preferences and actions. We also show that the textual portion
of the reviews can improve product sales prediction compared to a
baseline technique that simply relies on numeric data
Geography and Electronic Commerce: Measuring Convenience, Selection, and Price
We develop a formal model of online-offline retail channel substitution
to identify three factors that drive consumers to purchase online:
convenience, selection, and price. This model builds hypotheses on how
features of offline retail supply impact online purchasing. We then
examine how the local availability of offline retail options drives use
of the online channel and consequently how the convenience, selection,
and price advantages of the online channel may vary by geographic
location. In particular, we examine the effect of local store openings
on online book purchases in that location. We explore this problem using
data from Amazon on the top selling books for 1501 unique locations in
the US for 10 months ending in January 2006. In addition to this data,
we use information on changes in local retail competition as measured by
openings of large bookstores such as Borders or Barnes & Noble and
discount stores such as Wal-Mart or Target. We show that even
controlling for product-specific preferences by location, changes in
local retail options have substantial effects on online purchases. We
demonstrate how the convenience, selection, and price benefits of the
Internet are different for consumers in different types of locations.
More generally, we show that geography significantly impacts the benefit
that consumers derive from electronic markets
Search Costs, Demand Structure and Long Tail in Electronic Markets:Theory and Evidence
It is well known that the Internet has significantly reduced consumers'
search costs online. But relatively little is known about how search
costs affect consumer demand structure in online markets. In this paper,
we identify the impact of search costs on firm competition and market
structure by exploring a unique theoretical insight that search costs
create a kink in aggregate demand when firms change prices. The
significance of the kink reflects the magnitude of online search costs
and the kinked demand function provides information on how search costs
affect competition in the online market. Using a dataset collected from
Amazon and Barnes & Noble, we find that search costs vary
significantly across online retailers. Consumers face low search costs
for price information from Amazon.com. It leads to a higher price
elasticity when the firm reduces prices than when it increases prices,
increasing Amazon's incentive to engage in price competition. On the
other hand, consumers face relatively higher search costs for price
information from Barnes & Noble. This leads to a lower price
elasticity when Barnes & Noble reduces prices than when it increases
prices, reducing Barnes & Noble's incentive to engage in price
competition. We also find that search costs decrease with the passage of
time as the information about price changes dissipates among consumers,
leading to increased price elasticity over time. Finally, we highlight
that search costs are lower for popular books compared to rare and
unpopular books. These findings have implications for the impact of the
Internet on the Long Tail phenomenon