Understanding what motivates participation is a central theme in the research on open source software (OSS) development. Our study contributes by revealing how the different motivations of OSS developers are interrelated, how these motivations influence participation leading to performance, and how past performance influences subsequent motivations. Drawing on theories of intrinsic and extrinsic motivation, we develop a theoretical model relating the motivations, participation, and performance of OSS developers. We evaluate our model using survey and archival data collected from a longitudinal field study of software developers in the Apache projects. Our results reveal several important findings. First, we find that developers' motivations are not independent but rather are related in complex ways. Being paid to contribute to Apache projects is positively related to developers' status motivations but negatively related to their use-value motivations. Perhaps surprisingly, we find no evidence of diminished intrinsic motivation in the presence of extrinsic motivations; rather, status motivations enhance intrinsic motivations. Second, we find that different motivations have an impact on participation in different ways. Developers' paid participation and status motivations lead to aboveaverage contribution levels, but use-value motivations lead to below-average contribution levels, and intrinsic motivations do not significantly impact average contribution levels. Third, we find that developers' contribution levels positively impact their performance rankings. Finally, our results suggest that past-performance rankings enhance developers' subsequent status motivations.

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We study the offers submitted by consumers to a large Name-Your-Own-Price (NYOP) online retailer. A distinctive feature of this retailer is that it allows consumers to repeatedly submit offers on one and the same product. While consumers could identify the threshold price (the minimum price for which the retailer is willing to sell) by incrementing their offer in small steps in each consecutive round, such a strategy would require them to go through many additional online transactions. We define frictional cost as the disutility that the consumer experiences when conducting an online transaction, such as submitting an offer. Thus, in our setting, consumers trade off a direct financial value (lower price) for frictional costs. Based on a consumer choice model capturing this trade-off, we use the observed consumer behavior to reconstruct the frictional cost parameters for the consumers in our sample. We show that, perhaps contrary to the general wisdom, frictional costs in electronic markets are substantial, with median values ranging from EUR 3.54 for a portable digital music player (MP3) to EUR 6.08 for a personal digital assistant (PDA). We find that consumers who have gathered experience with the NYOP channel in previous transactions exhibit lower frictional costs than consumers who use the channel for the first time. Surprisingly, sociodemographic variables do not help to explain the variation in frictional costs.Electronic Markets, Frictional Costs, Name-Your-Own-Price Channel, Online Haggling, Price Dispersion

Previous research has examined whether price dispersion exists in theoretically highly efficient Internet markets. However, much of the previous work has been focused on industries with low cost and undifferentiated products. In this paper, we examine the presence of price dispersion and product differentiation using data on the airline ticket offerings of online travel agents (OTAs). We find that different OTAs offer tickets with substantially different prices and characteristics when given the same customer request. Some of this variation appears to be due to product differentiation-different OTAs specialize by systematically offering different trade-offs between ticket price and ticket quality (minimizing the number of connections, matching requested departure and return time). However, even after accounting for differences in ticket quality, ticket prices vary by as much as 18% across OTAs. In addition, OTAs return tickets that are strictly inferior to the ticket offered by another OTA for the same request between 2.2% and 28% of the time. Overall, this suggests the presence of both price dispersion and product differentiation in the online travel market. We would like to thank David Croson, Rachel Croson, Moti Levi, Eli Snir, Ho-Geun Lee, Rebecca Tsui, the participants of the Workshop on Information Systems and Economics, the Department Editor, Associate Editor, and five anonymous referees for helpful comments on earlier drafts of this paper. This paper has benefited from seminars at Carnegie Mellon University, MIT, New York University, Ohio State University, Stanford University, University of California at Berkeley, University of Maryland at College Park, University of Minnesota, University of Southern California, University of Texas at Austin, and University of Washington at Seattle, and the University of Pennsylvania. We would also like to thank Chad Ross for programming assistance. Funding for this work was provided by the Reginald Jones Center Project on Information: Industry Structure and Competitive Strategy. Keywords electronic markets, price dispersion, product differentiation AbstractPrevious research has examined whether price dispersion exists in theoretically highly efficient Internet markets. However, much of the previous work has been focused on industries with low cost and undifferentiated products. In this paper, we examine the presence of price dispersion and product differentiation using data on the airline ticket offerings of online travel agents (OTAs). We find that different OTAs offer tickets with substantially different prices and characteristics when given the same customer request. Some of this variation appears to be due to product differentiation --different OTAs specialize by systematically offering different tradeoffs between ticket price and ticket quality (minimizing the number of connections, matching requested departure and return time). However, even after accounting for differences in ticket quality, ticket prices vary by as much as 18% across OTAs. In addition, ...

The enhanced abilities of online retailers to learn about their customers' shopping behavior have increased fears of dynamic pricing, a practice in which a seller sets prices based on the estimated buyer's willingness-to-pay. However, among online retailers, a deviation from a one-price-for-all policy is the exception. When price discrimination is observed, it is often in the context of customer outrage about unfair pricing. One setting where pricing varies is the Name-Your-Own-Price (NYOP) mechanism. In contrast to a typical retail setting, in NYOP markets, it is the buyer who places an initial offer. This offer is accepted if it is above some threshold price set by the seller. If the initial offer is rejected, the buyer can update her offer in subsequent rounds. By design, the final purchase price is opaque to the public; the price paid depends on the individual buyer's willingness-to-pay and offer strategy. Further, most forms of NYOP employ a fixed threshold price policy. In this paper we compare a fixed threshold price setting with an adaptive threshold price setting. A seller who considers an adaptive threshold price has to weigh potentially greater profits against customer objections about the perceived fairness of such a policy. We first derive the optimal strategy for the seller. We analyze the effectiveness of an adaptive threshold price vis-à-vis a fixed threshold price on seller profit and customer satisfaction. Further, we evaluate the moderating effect of revealing the threshold price policy (adaptive vs. fixed) to buyers. We test our model in a series of laboratory experiments and in a large field experiment at a prominent NYOP seller involving real purchases. Our results show that revealing the usage of an adaptive mechanism yields higher profits and more transactions than not revealing this information. In the field experiment we find that applying a revealed adaptive threshold price can increase profits by over 20% without lowering customer satisfaction.