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Despite the recent misfortunes of many dotcoms, e-commerce will have major and lasting effects on economic activity. But the rise and fall in the valuations of the first wave of e-commerce companies show that vague promises of distant profits are insufficient. Only business models based on sound economic propositions will survive. This book provides professionals, investors, and MBA students the tools they need to evaluate the wide range of actual and potential e-commerce businesses at the microeconomic level. It demonstrates how these tools can be used to assess a variety of existing applications. Advances in web-based technology--particularly automation and delegation technologies such as smart agents, shopping bots, and bidding elves--support the further growth of e-commerce. In addition to enabling consumers to conduct automated comparisons and sellers to access visitors' background information in real time, such software programs can make decisions for individuals, negotiate with other programs, and participate in online markets. Much of e-commerce's economic value arises from this kind of automation, which not only reduces operating costs but adds value by generating new market interactions. This text teaches how to analyze the added value of such applications, considering consumer behavior, pricing strategies, incentives, and other critical factors. It discusses added value in several e-commerce arenas: online shopping, business-to-business e-commerce, application design, online negotiation (one-to-one trading), online auctions (one-to-many trading), and many-to-many electronic exchanges. Combining insights from several years of microeconomic research as well as from game theory and computer science, it stresses the importance of economic engineering in application design as well as the need for business models to take into account the "total game." As the only serious treatment of the microeconomics of e-commerce, this book should be read by anyone seeking e-commerce solutions or planning to work in the field.
stock markets. --- selling agents. --- retailers. --- protocols. --- procurement systems. --- personalization. --- online auctions. --- naïve bidders. --- matching process. --- marketplaces. --- loyalty. --- late bidding. --- information. --- incentives. --- game theory. --- exchanges. --- deadlines. --- competition. --- bilateral negotiations. --- auction design. --- alternating offers. --- adverse selection. --- Vickrey auction. --- ShopBots. --- English auctions. --- Dutch auctions. --- Bertrand paradox. --- Electronic commerce. --- naïve bidders.
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This handbook brings together the latest research on applied market design. It surveys matching markets: environments where there is a need to match large two-sided populations to one another, such as law clerks and judges or patients and kidney donors.
Game theory. --- Markets --- Statistical matching. --- Supply and demand. --- Mathematical models. --- Demand and supply --- Industrial production --- Law of supply and demand --- Economics --- Competition --- Exchange --- Overproduction --- Prices --- Value --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Sampling (Statistics) --- Public markets --- Commerce --- Fairs --- Market towns --- Games, Theory of --- Theory of games --- Mathematical models --- Mathematics --- Supply and demand --- Statistical matching --- Game theory --- E-books
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We use equity crowdfunding data to ask how fundraising amounts can be explained by what entrepreneurs ask for, versus what investors want to invest. The analysis exploits unique features of crowdfunding where entrepreneurs not only set investment goals, but also chose when to close their campaigns. More experienced and more educated founder teams ask for more. Their campaigns succeed more often, and they raise more money. Female teams ask for less, are equally successful, yet raise significantly less. They also wait longer before closing campaigns, suggesting they want to raise more than what they originally asked for.
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