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