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

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  • Sarath Tomy

    (Department of Computer Science and Information Technology, La Trobe University, Kingsbury Drive, Bundoora, VIC 3083, Australia)

  • Eric Pardede

    (Department of Computer Science and Information Technology, La Trobe University, Kingsbury Drive, Bundoora, VIC 3083, Australia)

Abstract

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.

Suggested Citation

  • Sarath Tomy & Eric Pardede, 2018. "From Uncertainties to Successful Start Ups: A Data Analytic Approach to Predict Success in Technological Entrepreneurship," Sustainability, MDPI, vol. 10(3), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:602-:d:133506
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    References listed on IDEAS

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    Cited by:

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    3. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
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    5. Grace C. Liu & Willem Spanjers, 2024. "Evaluating Gender Differences in the Effects of Ambiguity and Misperception on Entrepreneurship in Three Business Development Stages: A Panel Data Analysis," Working Paper series 24-13, Rimini Centre for Economic Analysis.
    6. Kim, Jongwoo & Kim, Hongil & Geum, Youngjung, 2023. "How to succeed in the market? Predicting startup success using a machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    7. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    8. Chang Hee Yoon & Francis Joseph Costello & Cheong Kim, 2019. "Assisting Sustainable Entrepreneurial Activities Through the Analysis of Mobile IT Services’ Success and Failure Factors," Sustainability, MDPI, vol. 11(20), pages 1-22, October.
    9. Mai, Nhat Chi, 2021. "A Comprehensive Model for Idea Evaluation at Early-Stage Level (Pre-Seed)," OSF Preprints axngj, Center for Open Science.
    10. Lupp, Daniel, 2023. "Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    11. Ronald Setty & Yuval Elovici & Dafna Schwartz, 2024. "Cost‐sensitive machine learning to support startup investment decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.

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