International Journal of Social Science & Economic Research
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Title:
SCORING CARD METHODOLOGIES FOR STARTUPS EVALUATION: A MACHINE LEARNING BASED APPROACH

Authors:
Athanasios Davalas

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Athanasios Davalas
University of the Aegean, Greece

MLA 8
Davalas, Athanasios. "SCORING CARD METHODOLOGIES FOR STARTUPS EVALUATION: A MACHINE LEARNING BASED APPROACH." Int. j. of Social Science and Economic Research, vol. 8, no. 12, Dec. 2023, pp. 3900-3914, doi.org/10.46609/IJSSER.2023.v08i12.013. Accessed Dec. 2023.
APA 6
Davalas, A. (2023, December). SCORING CARD METHODOLOGIES FOR STARTUPS EVALUATION: A MACHINE LEARNING BASED APPROACH. Int. j. of Social Science and Economic Research, 8(12), 3900-3914. Retrieved from https://doi.org/10.46609/IJSSER.2023.v08i12.013
Chicago
Davalas, Athanasios. "SCORING CARD METHODOLOGIES FOR STARTUPS EVALUATION: A MACHINE LEARNING BASED APPROACH." Int. j. of Social Science and Economic Research 8, no. 12 (December 2023), 3900-3914. Accessed December, 2023. https://doi.org/10.46609/IJSSER.2023.v08i12.013.

References

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ABSTRACT:
Startup companies need more support in order to anticipate at early stages their market competition and sustainability perspective finding alternative strategies to establish a balance between profitability, growth and control. Investors should be easier convinced if the Balanced Score Card reflects that current market, product and human capital status and allows predicting their performance in short or long term. To react to ever-changing market conditions, startups need to accept the investment to new technologies including artificial intelligence (AI). This paper proposes a Machine Learning approach for formulating the KPIs of the Balanced Scorecard for startups using an analytical hierarchal process and a learning-based classification system. The main innovation of this approach is that it allows collection of multiple source data for identifying the performance indicators, and it provides a flexible framework for selecting the right Machine Learning classifier to evaluate their performance.

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