Early growth technology analysis : case studies in solar energy and geothermal energy

Abstract

Public and private organizations try to forecast the future of technological developments and allocate funds accordingly. Based on our interviews with experts from MIT's Entrepreneurship Center, Sloan School of Management, and IBM, and review of literature, we found out that this important fund allocation process is dominated by reliance on expert opinions, which has important drawbacks alongside its advantages. In this Thesis, we introduce a data-driven approach, called early growth technology analysis, to technology forecasting that utilizes diverse information sources to analyze the evolution of promising new technologies. Our approach is based on bibliometric analysis, consisting of three key steps: extraction of related keywords from online publication databases, determining the occurrence frequencies of these keywords, and identifying those exhibiting rapid growth. Our proposal goes beyond the theoretical level, and is embodied in software that collects the required inputs from the user through a visual interface, extracts data from web sites on the fly, performs an analysis on the collected data, and displays the results. Compared to earlier software within our group, the new interface offers a much improved user experience in performing the analysis. Although these methods are applicable to any domain of study, this Thesis presents results from case studies on the fields of solar and geothermal energy. We identified emerging technologies in these specific fields to test the viability of our results. We believe that data-driven approaches, such as the one proposed in this Thesis, will increasingly be used by policy makers to complement, verify, and validate expert opinions in mapping practical goals into basic/applied research areas and coming up with technology investment decisions.