A Hybrid Business Success Versus Failure Classification Prediction Model: A Case of Iranian Accelerated Start-ups

Abstract

The purpose of this study is to reduce the uncertainty of early stage startups success prediction and filling the gap of previous studies in the field, by identifying and evaluating the success variables and developing a novel business success failure (S/F) data mining classification prediction model for Iranian start-ups. For this purpose, the paper is seeking to extend Bill Gross and Robert Lussier S/F prediction model variables and algorithms in a new context of Iranian start-ups which starts from accelerators in order to build a new S/F prediction model. A sample of 161 Iranian start-ups which are based in accelerators from 2013 to 2018 is applied and 39 variables are extracted from the literature and organized in five groups. Then the sample is fed into six well-known classification algorithms. Two staged stacking as a classification model is the best performer among all other six classification based S/F prediction models and it can predict binary dependent variable of success or failure with accuracy of 89% on average. Also finding shows that “starting from Accelerators”, “creativity and problem solving ability of founders”, “fist mover advantage” and “amount of seed investment” are the four most important variables which affects the start-ups success and the other 15 variables are less important.