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Models and Tools for Technology Start-Up Companies Success Analysis

Author

Listed:
  • Kaloyan Haralampiev
  • Boyan Yankov
  • Petko Ruskov

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.

Suggested Citation

  • Kaloyan Haralampiev & Boyan Yankov & Petko Ruskov, 2014. "Models and Tools for Technology Start-Up Companies Success Analysis," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 15-24, October.
    • Handle: RePEc:nwe:eajour:y:2014:i:3:p:15-24
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    References listed on IDEAS

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    1. James J. Chrisman & Alan Bauerschmidt & Charles W. Hofer, 1998. "The Determinants of New Venture Performance: An Extended Model," Entrepreneurship Theory and Practice, , vol. 23(1), pages 5-29, October.
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    Cited by:

    1. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    2. Dafei Yin & Jing Li & Gaosheng Wu, 2021. "Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods," Papers 2112.07985, arXiv.org.
    3. Macalik Joanna, 2023. "Personal Brand — Instructions of Use. Do Young Professionals Want and Need to be Taught Personal Branding?," Marketing of Scientific and Research Organizations, Sciendo, vol. 28(2), pages 41-60, June.
    4. Stanley Ibeku, 2018. "Organisational Learning, Innovation And Performance Of Technology Start-Ups In Lagos, Nigeria," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-15, April.

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    More about this item

    Keywords

    technology start-ups; success prediction; prediction tool; start-ups analysis;
    All these keywords.

    JEL classification:

    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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