From Uncertainties to Successful Start Ups: A Data Analytic Approach to Predict Success in Technological Entrepreneurship

Understanding uncertainties and assessing the risks surrounding business opportunities is essential to support the success of sustainable entrepreneurial initiatives launched on a daily basis. The contribution of this study is the identification of uncertainties surrounding opportunities in the opportunity evaluation stage of the entrepreneurial process and the examination of how the analysis and evaluation of uncertainty factors, with the help of data, can predict the future success of an organization. In the first phase, the uncertainty factors are classified based on their sources and we discuss the likely implications towards new venture success with the help of existing literatures. In the second phase, a success prediction model is implemented using machine learning techniques and strategic analysis. The model is trained in such a way that, when new data emerges, the qualitative data is transformed into quantitative data and the probability of success or failure is calculated as the result output in the pre-start-up phase. The method and findings would be relevant for nascent entrepreneurs and researchers focusing on sustainable technology entrepreneurship.