Supporters of net neutrality have often argued that more competition among Internet service providers (ISPs) is beneficial for an open Internet and that the market power of the ISPs lies at the hea...
The literature highlights the importance of product review information to a consumer, but limited knowledge exists on the provision of such information in a shopping website. This research uses inf...
With the increasing popularity of product recommendation networks in e-commerce, researchers and practitioners are eager to understand how they can strategically manage product assortments through ...
This paper seeks to develop a better understanding of job performance in the context of a knowledge management system (KMS) implementation. This work adopts the context theorizing approach that inf...
Information technology outsourcing (ITO) is the predominant mode of acquiring information systems services, providing clear evidence that the economics of service delivery favor external service pr...
Online retailing is dominated by a channel structure in which a retailer either buys products from competing manufacturers and resells to consumers (wholesale scheme) or lets manufacturers sell dir...
Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a...
Recently, large companies have been experimenting with corporate prediction markets run among their employees. In the present study, we develop an analytical model to analyze the effects of information precision and social interactions on prediction market performance. We find that increased precision of public information is not always beneficial to the prediction market accuracy because of the “hidden profiles” effect: the information-aggregation mechanism places a larger-than-efficient weight on existing public information. We show that a socially embedded prediction market with information sharing among participants may help correct such inefficiency and improve prediction market performance. We also identify conditions under which increased precision of public information is detrimental in a nonnetworked prediction market and in a socially embedded prediction market. These results should be of interest to practitioners as the managerial implications highlight the detrimental effect of public information and the role of social networking among employees in a corporate prediction market.
Prediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief–decision framework as the event probability estimator. This belief–decision framework models bettors’ beliefs with mixed beta distributions and bettors’ decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.