Models and Tools for Technology Start-Up Companies Success Analysis

Abstract

The designs presented in the article are fastened in the authors’ years-long research on entrepreneurship and business model innovations. A quantitative research was performed to derive a model for predicting the success of Bulgarian startup companies. The authors started this research with in-depth inquiries of start-up companies in Bulgaria. Under our guidance, several research analysts investigated each start-up using approximately 100 questions. The preceding research stages included an overview and an analysis of existing success prediction models, a new abstract success prediction model, a venture creation process model and a qualitative research. The abstract success prediction model was extended with measurable variables with the help of a quantitative research of Bulgarian entrepreneurs. The current dataset of companies has been enriched with more cases and has been analyzed using data mining software: IBM SPSS Modeler, which automatically tests different models and suggests the best performing ones and also with the open source product Weka. The best derived model is a classification tree that correctly predicts the success of technology start-ups from the dataset in 83,76% of the test cases. The analysis revealed the answers to challenges and questions that start-up companies face and implemented a model that was deployed into an information system for start-ups success prediction. The developed information system will help to predict the success of start-ups. The software will evolve iteratively, and by involving more companies to use it, will grow its database.