Prediction Markets as Web 2.0 Tools for Enterprise 2.0

In today’s fast-changing business environment, companies and stakeholders are confronted with a new range of business decisions. These decisions are constrained by events that are out of the scope of competencies of the decision makers. In this context, prediction markets, which have demonstrated their efficiency in predicting the outcome of major elections, could support companies in leveraging the knowledge and competencies of their collaborators. Using the power of Web 2.0 and the participative collaboration of the crowd, prediction markets are well designed to assess this new range of decisions. The simple buy and sell mechanism uses the crowd as proxy toward various information sources. Moreover, the decision task is distributed between the traders, leaving them the tasks of framing the problem, setting the evaluation criteria and collecting the supporting information, aggregating their results through the current market price. This paper presents the capabilities of prediction market as Web 2.0 decision making tool to increase the efficiency of the decision making process in Enterprise 2.0 companies. It relies on many characteristics of these companies such as the enhanced collaboration between dispersed collaborators through social networks tools, the commitment of all collaborators in the knowledge management process and the dedication of the organization toward open and participatory decision processes. It also have the ability to promptly leverage pieces of information scattered through the organization, without requesting a single stakeholder knowing what to search for and where to search it. Moreover, due to the engagement of a large crowd in the decision process, final decisions are subject to a broaden adhesion within the company. We conclude by making propositions regarding the deployment of this decision making tool. This involves numerous managerial issues, but also opens new perspectives to build upon the collective knowledge of the enterprise.