Decision support systems (DSS) are aimed at helping decision makers in devising appropriate solutions to business problems while negotiation support systems (NSS) are aimed at assisting stakeholders in reaching mutually satisfactory decisions. The successful use of these systems involves a combination of human ability and computer support. An implicit assumption underlying their use is that the business problems for which they are deployed have been carefully defined by the stakeholders prior to their use. This includes, in particular, understanding of the problem variables and their possible impact on the decision outcomes and knowing which information is necessary for supporting it. However, this assumption might not hold in the modern business environment. This is due to the increasing complexity and rate of change of the environment, the geographical and temporal dispersion of modern organizations, and the diversity and quantity of information sources that is available. In this paper we propose the idea of extending the scope of these systems to include a step preceding the solution process: problem framing. We claim that new technologies such as Web 2.0 provide novel opportunities to add this facility to DSS and NSS.

The full paper is available in the publications section

06.01.12 - Using Ontologies and Soft Systems Methodology to Provide Multi-user Support in Problem Structuring (pdf)

We present and compare two original approaches for technology assessment and foresight based on opposite paradigm: a management science approach (Multi-Criteria Decision-Making) versus a participatory approach (Prediction Market). These approaches are intended to support the management of a technology portfolio and the assessment of new technology by an IT organization. In order to explore the relevance of the research, we conducted several experiments in real environments. The results demonstrated that the rigor of management science and the participation of the Web 2.0 approach are complementary strengths for technology foresight. Furthermore, a framework has been established to compare the two approaches.

The full paper is available in the publications section

14.12.08 - Comparison of Multicriteria and Prediction Market Approaches for Technology Foresight (pdf)

R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy and the design issues to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process.

The full paper is available in the publications section

17.06.08 - Prediction markets as an innovative way to manage R&D portfolios (pdf)

R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies, there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process.

The full paper is available in the publications section

06.06.08 - Prediction markets as an innovative way to manage R&D portfolios (pdf)