Knowledge that Transforms

Current knowledge management (KM) technologies and strategies advocate two different approaches: knowledge codification and knowledge-sharing networks. However, the extant literature has paid limited attention to the interaction between them. This research draws on the literature on formal modeling of networks to examine the interaction between knowledge codification and knowledge-sharing networks. The analysis suggests that an increase in codification may damage existing network-sharing ties. Anticipating that, individuals may hoard their knowledge to protect their network ties, even when there are nontrivial rewards for codification. We find that despite the aforementioned tension between the codification and the network approach, a firm may still benefit from combining the two approaches. Specifically, when the future sharing potential between knowledge workers is high, a combination of the two approaches may outperform a codification-only or a network-only approach because the codification reward causes fewer network ties to break down, and the benefit from increased codification can offset the loss of some network ties. However, when the future sharing potential is low, an increase in codification reward can quickly break down the whole network. Thus, firms may be better off by pursuing a codification-only or a network-only strategy.

Keyword advertising, including sponsored links and contextual advertising, powers many of today's online information services such as search engines and Internet-based emails. This paper examines the design of keyword auctions, a novel mechanism that keyword advertising providers such as Google and Yahoo! use to allocate advertising slots. In our keyword auction model, advertisers bid their willingness-to-pay per click on their advertisements, and the advertising provider can weight advertisers' bids differently and require different minimum bids based on advertisers' click-generating potential. We study the impact and design of such weighting schemes and minimum-bid policies. We find that weighting scheme determines how advertisers with different click-generating potential match in equilibrium. Minimum bids exclude low-valuation advertisers and at the same time may distort the equilibrium matching. The efficient design of keyword auctions requires weighting advertisers' bids by their expected click-through-rates, and requires the same minimum weighted bids. The revenue-maximizing weighting scheme may or may not favor advertisers with low click-generating potential. The revenue-maximizing minimum-bid policy differs from those prescribed in the standard auction design literature. Keyword auctions that employ the revenue-maximizing weighting scheme and differentiated minimum bid policy can generate higher revenue than standard fixed-payment auctions. We draw managerial implications for pay-per-click and other pay-for-performance auctions and discuss potential applications to other areas.

Historically, the use of peer-to-peer (P2P) networks has been limited primarily to user-initiated exchanges of (mostly music) files over the Internet. This traditional view of P2P networks is changing, however, and the use of P2P networks has been suggested for delivering general-purpose content over the Web (or corporate intranets), even in real time. We analyze sharing in a P2P community in this new context under three different congestion measures: delay, jitter, and packet loss. Sharing is important to study in the presence of congestion because most existing research on P2P networks views congestion in the network as a relatively insignificant criterion. However, when delivering general-purpose content, congestion and its relationship to sharing is a critical factor that influences end-user performance. This paper looks at P2P networks from this new perspective by explicitly considering the effects of congestion on user incentives for sharing. We also propose a simple incentive mechanism that induces socially optimal sharing.

The World Wide Web has become a key intermediary between producers and consumers of information. Web's linkage structure has been exploited by contemporary search engines to decrease the search cost for consumers while usually also rewarding the producers of higher status Web pages. In addition to influencing visibility and accessibility, in-links, as marks of recognition, accord status to a Web page. In this paper we show how Web page status may be predicted at least in part by page location and topic specificity. Moreover, we observe that the “philanthropic” contributions of a Web page—specifically, contributions of information brokerage function—are also good predictors of in-links. The observations are made in the presence of domain- and topic-specific effects. Interestingly, all of these features that may predict status are “local” to a given Web page and within the control of the owner/author of the page. This is in contrast to the “global” nature of Web linkage-based metrics such as in-link count that are derived as a result of downloading and indexing billions of pages. Because the linkage structure of the Web affects browsing, crawling, and retrieval, our results have implications for vertical and general search, business intelligence, and content management.

In peer-to-peer (P2P) media distribution, users obtain content from other users who already have it. This form of decentralized product distribution demonstrates several unique features. Only a small fraction of users in the network are queried when a potential adopter seeks a file, and many of these users might even free-ride, i.e., not distribute the content to others. As a result, generated demand might not always be fulfilled immediately. We present mixing models for product diffusion in P2P networks that capture decentralized product distribution by current adopters, incomplete demand fulfillment and other unique aspects of P2P product diffusion. The models serve to demonstrate the important role that P2P search process and distribution referrals—payments made to users that distribute files—play in efficient P2P media distribution. We demonstrate the ability of our diffusion models to derive normative insights for P2P media distributors by studying the effectiveness of distribution referrals in speeding product diffusion and determining optimal referral policies for fully decentralized and hierarchical P2P networks.

One of the distinctive features of sites on the Internet is their ability to gather enormous amounts of information about their visitors and to use this information to enhance a visitor's experience by providing personalized information or recommendations. In providing personalized services, a website is typically faced with the following trade-off: When serving a visitor's request, it can deliver an optimally personalized version of the content to the visitor, possibly with a long delay because of the computational effort needed, or it can deliver a suboptimal version of the content more quickly. This problem becomes more complex when several requests are waiting for information from a server. The website then needs to trade off the benefit from providing more personalized content to each user with the negative externalities associated with higher waiting costs for all other visitors that have requests pending. We examine several deterministic resource allocation policies in such personalization contexts. We identify an optimal policy for the above problem when requests to be scheduled are batched, and show that the policy can be very efficiently implemented in practice. We provide an experimental approach to determine optimal batch lengths, and demonstrate that it performs favorably when compared with viable queueing approaches.