Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews, and more important, what kind of reviews do such interactions induce? We study these questions using data from epinions.com, one of the largest product review websites where users can subscribe to one another. By applying both panel data and flexible matching methods, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings systematically change, and become more negative and more varied. Such tradeoff has not been previously documented, and has important implications for not just product review websites, but user-generated content sites as well.
Business-to-consumer online auctions form an important element in the portfolio of mercantile processes that facilitate electronic commerce activity. Much of traditional auction theory has focused on analyzing single-item auctions in isolation from the market context in which they take place. We demonstrate the weakness of such approaches in online settings where a majority of auctions are multiunit in nature. Rather than pursuing a classical approach and assuming knowledge of the distribution of consumers' valuations, we emphasize the largely ignored discrete and sequential nature of such auctions. We derive a general expression that characterizes the multiple equilibria that can arise in such auctions and segregate these into desirable and undesirable categories. Our analytical and empirical results, obtained by tracking real-world online auctions, indicate that bid increment is an important factor amongst the control factors that online auctioneers can manipulate and control. We show that consumer bidding strategies in such auctions are not uniform and that the level of bid increment chosen influences them. With a motive of providing concrete strategic directions to online auctioneers, we derive an absolute upper bound for the bid increment. Based on the theoretical upper bound we propose a heuristic decision rule for setting the bid increment. Empirical evidence lends support to the hypothesis that setting a bid increment higher than that suggested by the heuristic decision rule has a negative impact on the auctioneer's revenue.Online auctions, Dynamic pricing, Pricing mechanisms
UGC (User-generated content) websites routinely deploy incentive hierarchies, where users achieve increasingly higher status in the community after achieving increasingly more difficult goals, to motivate users to contribute. Yet the existing empirical literature remains largely unclear whether such hierarchies are indeed effective in inducing user contributions. We gathered data from a large online crowd-based knowledge exchange to answer this question, and drew on the goal setting theory to study users' contributions before and after they reach consecutive levels of a vertical incentive hierarchy. We found evidence that even though these "glory"-based incentives may motivate users to contribute more before the goals are reached, user contribution levels dropped significantly after that. In other words, the cumulative effect appears only temporary. Our results hence highlight some unintended and heretofore undocumented effects of incentive hierarchies, and have important implications for business models that rely on user contributions, such as knowledge exchange and crowdsourcing, as well as the broader phenomenon of "gamification" in other contexts.
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