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The purpose of this paper is to explore the use of information aggregation markets (IAMs) for community‐based idea management and to present IDeM, a novel Internet‐based software tool that can be used for generating and evaluating new ideas utilizing the concept of IAMs.

Starting with a review of existing methods for collective intelligence, IAMs are identified as a prominent method for collective intelligence. Specific requirements for exploring IAMs for idea management are derived. Based on these requirements, a software tool for implementing IAMs in the context of idea management is developed (IDeM). IDeM has been evaluated and evaluation results are used to identify IDeM's benefits and limitations. A review of related work points out the innovative characteristics of IDeM.

Evaluation results indicate that IAMs is an efficient method for idea generation and evaluation. Moreover IDeM is perceived both as easy to use and efficient in supporting idea generation and evaluation.

IDeM can be used by commercial or other organizations for supporting generation and evaluation of new ideas.

IDeM's innovative aspects are: in addition to trading, it allows users involvement by means of new idea submission, rating of ideas and commenting on ideas; it confronts the uncertainty of new idea related events by offering an expert based valuation mechanism.; and it extends the typical output of IAM tools – which is price of idea‐stocks – by calculating the volume weighted average price.

The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions.

The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field.

The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, analysis suggests that there is a clear need for more advanced sentiment analysis methods as well as methods for identifying search terms for collection and filtering of raw SM data.

The proposed framework enables researchers to classify and evaluate existing studies, to design scientifically rigorous new studies and to identify the field's weaknesses, hence proposing future research directions.