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A nonparametric decision approach for entrepreneurship

Author

Listed:
  • Bing Xu

    (Zhejiang Gongshang University)

  • Jingwen Yang

    (Zhejiang Gongshang University)

  • Bifei Sun

    (Zhejiang Gongshang University)

Abstract

Credit identification is one of core issues of financing process. Enterprise credit involves a lot of financial and non-financial measures, among which entrepreneurship is an important but rarely mentioned variable. Good entrepreneur credit often leads to good enterprise credit. A comprehensive analysis of enterprise credit identification is important to avoid losses, foster excellent enterprise and make the optimal allocation of resources. The existing literature mainly studied the impact of entrepreneurship on enterprise credit from the perspective of historical information, which is about average and tendency. Hence, those models were unable to explain the function of complex human nature and, consequently, linear models are unable to well describe the relationship between enterprise credit and entrepreneur credit. Given the deficiency of parametric models when discussing the impact of entrepreneur credit, a non parametric approach are proposed to individually describe the impact path of different individuals. This paper established a decision tree based on nonparametric approach to verify the practicability of the model in the evaluation of enterprise credit recognition. In the end of this paper, we demonstrate the validity of the non parametric model and the validation method of it.

Suggested Citation

  • Bing Xu & Jingwen Yang & Bifei Sun, 2018. "A nonparametric decision approach for entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 14(1), pages 5-14, March.
    • Handle: RePEc:spr:intemj:v:14:y:2018:i:1:d:10.1007_s11365-017-0465-4
    DOI: 10.1007/s11365-017-0465-4
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    References listed on IDEAS

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    3. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
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    Cited by:

    1. Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    2. Xuefang Xie & Xuemei Xie & Carla Martínez-Climent, 2019. "Identifying the factors determining the entrepreneurial ecosystem of internet cultural industries in emerging economies," International Entrepreneurship and Management Journal, Springer, vol. 15(2), pages 503-522, June.
    3. Francisco-José Cossío-Silva & María-Ángeles Revilla-Camacho & Beatriz Palacios-Florencio & Dolores Garzón Benítez, 2019. "How to face a political boycott: the relevance of entrepreneurs’ awareness," International Entrepreneurship and Management Journal, Springer, vol. 15(2), pages 321-339, June.
    4. Márton Gosztonyi & Csákné Filep Judit, 2022. "Profiling (Non-)Nascent Entrepreneurs in Hungary Based on Machine Learning Approaches," Sustainability, MDPI, vol. 14(6), pages 1-20, March.

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