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Classic vs. New Econometric Models in Startup Assessment

Econometric models have been traditionally used as a means of evaluating the chances of startup success for entrepreneurs and investors alike – it allowed for making an educated estimation on whether the investment would prove true or false. This type of evaluation relies on the assessment of both quantifiable and non-quantifiable qualities of the startup and the individuals involved in it. Recently, a new model started to emerge, based on machine learning predictions, sporting a higher reliance on quantifiable data and less so on interpretations. The purpose of the proposed research is to compare both models and its added value using the same startup sample while also examining the influence both models have on startups and investors alike.

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Research Question

The research questions to be answered in this study are as follows:

  • Does AI bring added value compared to classic econometric models for enterprises and startups?
  • How do investors and startup initiators perceive both tools and how does it affect their performance?

Background and Literature Review

Econometric model of startup evaluation typically relies on 3 groups of factors to evaluate the chances of it succeeding or failing. These include the personal characteristics of the entrepreneur, key factors of strategy, and the business environment (Diaz-Santamaria & Buclchand-Gidumal, 2021). Some of the identified items include previous experience, training, and skills of the entrepreneur, startup age, size, strategy, and financial health, as well as market conditions, location, and government support (Diaz-Santamaria & Buclchand-Gidumal, 2021). While the econometric model does have certain qualitative measurements to it, such as evaluations of strategy selected, the majority of the arguments are still rooted in quantitative and measurable effects and outcomes.

Bento (2017) offer a study of how machine-learning algorithms can predict the success or failure of startups based on measurable and quantifiable data. The variables the program utilizes include the presence of competitors, investors, funding, and venture capital, among others. Intrapreneurial qualities are also measured based on age, experience, mentions in CrunchBase articles, family experience, and others (Bento, 2017). The model is similar to the econometric approach, with a difference being weakness in asserting data that requires interpretation (Bento, 2017). The accuracy of predictions was between 88% and 95% (Bento, 2017).

Zlatcovic (2018) shows that an econometric approach can be applied to various non-material components of the company, such as human resources. This research finds the presence of quality human resources to be a factor contributing to success the most, along with location and innovation strategy (Zlatkovic, 2018). Machine learning can perform the same actions and evaluations. Mullainathan and Spiess (2017) state that the majority of econometric models can be readily converted into code and implement machine learning protocols to great success. Their estimations are backed by the later studies of Bento (2017) with their 88-95% successful prediction rates.

Methodology and Approach

The proposed methodology for this research is a mixed study, which will include both qualitative and quantitative data to answer the research questions (Schoonenboom & Johnson, 2017). Quantitative data analysis will include econometric and machine learning evaluations of 100 startups in the technology sector, initiated in 2021. Qualitative data will be extracted from the interviews with investors and startup initiators alike to learn about their opinions on each system and on how it would affect their behavior. The interview will be comprised out of close-ended and open-ended questions to obtain both quantitative and qualitative data. The former will be analyzed using standard statistical methods, such as the T-test, whereas the latter will be analyzed using theme and content analysis (Brannen, 2017).

References

Bento, F. R. D. S. R. (2018). Predicting start-up success with machine learning. Web.

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Brannen, J. (Ed.). (2017). Mixing methods: Qualitative and quantitative research. Routledge.

Díaz-Santamaría, C., & Bulchand-Gidumal, J. (2021). Econometric estimation of the factors that influence startup success. Sustainability, 13(4), 2242.

Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.

Schoonenboom, J., & Johnson, R. B. (2017). How to construct a mixed methods research design. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 69(2), 107-131.

Zlatković, M. (2018). an econometric analysis of the effects of human resources and other factors on firm creation. Economic Themes, 56(4), 453-473.

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StudyCorgi. (2022, September 30). Classic vs. New Econometric Models in Startup Assessment. Retrieved from https://studycorgi.com/classic-vs-new-econometric-models-in-startup-assessment/

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StudyCorgi. (2022, September 30). Classic vs. New Econometric Models in Startup Assessment. https://studycorgi.com/classic-vs-new-econometric-models-in-startup-assessment/

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"Classic vs. New Econometric Models in Startup Assessment." StudyCorgi, 30 Sept. 2022, studycorgi.com/classic-vs-new-econometric-models-in-startup-assessment/.

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StudyCorgi. "Classic vs. New Econometric Models in Startup Assessment." September 30, 2022. https://studycorgi.com/classic-vs-new-econometric-models-in-startup-assessment/.

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StudyCorgi. 2022. "Classic vs. New Econometric Models in Startup Assessment." September 30, 2022. https://studycorgi.com/classic-vs-new-econometric-models-in-startup-assessment/.

References

StudyCorgi. (2022) 'Classic vs. New Econometric Models in Startup Assessment'. 30 September.

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