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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. Sheetal Malik & Praveen Sahu, 2024. "Significant entrepreneurial factors: a regional perspective," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 14(1), pages 1-10, December.
    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. 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.
    4. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    5. 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. 48(2), pages 41-60, June.

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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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