A multiple correspondence analysis model for evaluating technology foresight methods

Technology foresight (TF) studies the appropriate extrapolation methodologies for predicting the most likely technology development scenarios in the future. Although there is a vast literature dealing with the classification and development of technology foresight methods (TFMs), the problem of selecting those that best reflect the characteristics of an organization is challenging and remains mostly overlooked. We propose a TFM evaluation procedure that allows decision makers and managers to successfully address this problem. The proposed procedure identifies the most relevant TFMs and organizational criteria and uses them in a multiple correspondence analysis (MCA) model to select the most suitable method(s) for implementation. The proposed MCA model combines the doubling data technique with a row principal scoring procedure to allow for the reduction of dimensionality and, consequently, the graphical analysis of the patterns of relationships among TFMs and evaluation criteria. We present a case study in a knowledge-based organization to demonstrate the applicability and efficacy of the proposed evaluation procedure. The results show that the proposed model can be properly adapted to allow for a wide range of applications involving business organizations and government agencies