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March 5: Amrit Tiwana to speak on The Influence of Software Platform Modularity on Platform Abandonment: An Empirical Study of Firefox Extension Developers

February 23, 2010 By

Amrit Tiwana

Associate Professor,

Iowa State University

March 5, 2010

Alter Hall 405, 1000am – 1130am

Abstract

With competition increasingly among rival software platforms, retaining third-party developers outside firm boundaries is increasingly important. Such “module” developers often provide critically-differentiating technical innovations and new ideas, thus are vital to a platform’s success. This study addresses the underexplored question of how and why platform modularity—both technical and organizational—influences platform abandonment by developers. We introduce the notion of systems integration costs—which comprise both cross-module integration and module-platform integration—as a key explanatory construct in our nomological network.

We develop three ideas, building on modular systems theory. First, a decrease in systems integration costs decreases the likelihood of platform abandonment by module developers. Second, different facets of technical modularity differentially impact systems integration costs. Third, these relationships are moderated by organizational modularity i.e., how authority over technical decisions is shared between a module developer and the platform owner. Tests using data from developers of 342 modules for Mozilla’s Firefox browser platform largely support the proposed ideas.

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April 16: Chris Dellarocas to speak on Double Marginalization in Performance-Based Advertising: Implications and Solutions

February 17, 2010 By

Chris Dellarocas

Associate Professor,

Boston University

April 16, 2010

Alter Hall 405, 1000am – 1130am


Abstract

An important current trend in advertising is the replacement of traditional pay-per-exposure (pay-per-impression) pricing models with performance-based mechanisms in which advertisers pay only for measurable actions by consumers. Such pay-per-action (PPA) mechanisms are becoming the predominant method of selling advertising on the Internet. Well-known examples include pay-per-click, pay-per-call and pay-per-sale. This work highlights an important, and hitherto unrecognized, side-effect of PPA advertising. I find that, if the prices of advertised goods are endogenously determined by advertisers to maximize profits net of advertising expenses, PPA mechanisms induce firms to distort the prices of their goods (usually upwards) relative to prices that would maximize profits in settings where advertising is sold under pay-per-exposure methods. Upward price distortions reduce both consumer surplus and the joint publisher-advertiser profit, leading to a net reduction in social welfare. They persist in current auction-based PPA mechanisms, such as the ones used by Google and Yahoo. In the latter settings they always reduce publisher revenues relative to pay-per-exposure methods. In extreme cases they also lead to rat-race situations where, in their effort to outbid one another, advertisers raise the prices of their products to the point where demand for them drops to zero. I show that these phenomena constitute a form of double marginalization and discuss a number of enhancements to today’s PPA mechanisms that restore equilibrium pricing of advertised goods to efficient levels.

For a copy of the paper, click here.

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April 2: Anindya Ghose to speak on Estimating Demand in the Hotel Industry by Mining User-Generated and Crowdsourced Content

February 2, 2010 By

Anindya Ghose

Assistant Professor,

Stern School of Business, NYU

April 2, 2010

Alter Hall 746, 1000am – 1130am

Abstract

User-Generated Content (UGC) is changing the way consumers shop for goods. It is increasingly being recognized that the textual content of product reviews is an important determinant of consumers’ choices, over and above any numeric information. Similarly, websites that facilitate the creation of social tags by users can influence the desirability of a product or service. Moreover, one can harness the collective wisdom of the crowds by eliciting consumer opinions through on-demand user-contributed surveys. Based on a unique dataset of hotel reservations over a 3-month period from Travelocity.com, we estimate the demand for hotels using a structural model that incorporates information from different kinds of UGC. Data on UGC is obtained from three sources: (i) text of hotel reviews from two well-known travel search engines, Travelocity.com and Tripadvisor.com, (ii) social geo-tags identifying the different location-based attributes of hotels from Geonames.org, and (iii) on-demand user-contributed opinions on the most important hotel characteristics from Amazon Mechanical Turk. These data sources are merged with satellite images of the different hotel locations to create one comprehensive dataset summarizing the location and service characteristics of the hotels in our sample. We use text-mining techniques to incorporate textual information from user reviews in our estimation. We supplement these methods with image classification techniques and on-demand user-generated annotations. We estimate a two-step random coefficient structural model to infer the weight that consumers place on different location and service-related features of hotels. We also quantify how the extent of subjectivity, readability, complexity and other stylistic features of user-generated reviews affect hotel room sales. We use these estimates to compute the average consumer surplus from transactions in each hotel. Based on the estimation of consumer surplus, we propose a new ranking system for displaying hotels in response to a search query on a travel search engine. By doing so, one can provide customers with the “best-value” hotels early on, thereby improving the quality of online hotel search compared to existing systems. Several experiments with users suggest that our ranking system does better than existing systems.

For a copy of the complete paper, please email swattal@temple.edu