A Framework for a Valuation of Digital Start-Ups Using Artificial Intelligence and Fuzzy Sets

Pešík Jiří (), Procházková Petra Taušl () and Januška Martin ()
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Pešík Jiří: University of West Bohemia, Faculty of Economics, Department of Business Administration and Management, Univerzitní 22, 306 14, Plzeň, Czech Republic
Procházková Petra Taušl: University of West Bohemia, Faculty of Economics, Department of Business Administration and Management, Univerzitní 22, 306 14, Plzeň, Czech Republic
Januška Martin: University of West Bohemia, Faculty of Economics, Department of Business Administration and Management, Univerzitní 22, 306 14, Plzeň, Czech Republic

ACC Journal, 2023, vol. 29, issue 2, 71-83

Abstract: The purpose of this article is to present an innovative framework for assessing digital start-ups and smaller companies using a fuzzy set approach, considering the founder’s expertise, product marketability, financial health, and social media presence. Four Czech digital startups were analyzed by both human experts and an artificial intelligence model. The methodology is based on using a fuzzy additive ratio assessment. Each start-up was evaluated on a five-point scale, with the results compared to Deloitte’s FAST 50 rank. The AI and human evaluations differed, with humans placing more emphasis on the founder’s experience and product appeal. As a practical contribution, the article suggests a valuation framework involving both human and AI expertise for interactive comparison and update. The article highlights the value of AI in start-up assessment, stressing the necessity of merging human and artificial intelligence in decision-making. To date of the publication, no study combining human expertise and artificial intelligence using fuzzy sets was found. Therefore, both methodology and results can be considered innovative and original.

Keywords: Digital start-ups; Artificial intelligence; Fuzzy sets; Company evaluation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:accjnl:v:29:y:2023:i:2:p:71-83:n:1006

DOI: 10.2478/acc-2023-0006

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