A Study on Crowd Funding via Social Media and its Differences from Traditional Funding Methods

This paper looks into the emerging realm of social media and crowd funding as a viable option for start-up funding. A literature review revealed a key gap area where traditional funding methods are ill-suited (between personal/private funding and more commercial investors seeking a profit) but showed a relative lack of published information on crowd funding, likely because the concept is only a few years old. Five interviews were given with the leads of several start-up projects: three who used crowd funding through Kickstarter, a successful online start-up itself, and two who used traditional methods. These interviews were used to analyze what makes a project appropriate for crowd funding, the strengths and limitations of the platform, and what side benefits like market research and customer engagement entrepreneurs can expect to gain by using this method. Future work could include a quantitative decision model to choose the best funding method for any given project

Microprocessor Technology Forecasting using TFDEA

Technological advancements in the microprocessor industry are benchmarked and gauged against a set of diverse criteria specific to the fabrication process, usage as well as achieved performance. The changing trends in the appeal factor as well as wide variety of growing application of microprocessors in different industries can have a defining impact in the advancement of the technological features in future. This study is built on previous investigations in forecasting microprocessors’ technology and uses Technology Forecasting using Data Envelopment Analysis (TFDEA) methodology. It incorporates multiple variables that are affecting microprocessors’ future industry based on market priorities and customer’s specification preferences and aims at forecasting future microprocessor technology trends. The research uses the dataset collected by Stanford University, which holds a rich collection of microprocessor specifications from 17 factories for the past four decades, and is more recent compared to the dataset used in the former research. The result is a rate of change (RoC) that is based on much more recent dataset including the new generation of microprocessors (i.e. multi cores) and can be used to forecast the future microprocessor technology trends