Venture capitalists and angel investors usually apply a set of assessment criteria to evaluate the key elements of entrepreneurial projects. However, since each investor considers different criteria, previous researchers who analysed investors’ decision making, ended up analysing a variety of divergent aspects. In this paper, a systematic literature review on the assessment criteria applied by equity investors was carried out. The purpose of this study was to identify and classify all the criteria considered by previous researchers to determine whether some aspects were investigated more extensively than others and to understand the reasons for this type of approach. After screening the abstracts of 894 journal publications, 53 articles were selected for a detailed analysis. In total, 208 unique criteria were identified and were subsequently classified into 35 specific categories, 11 generic classes and 4 main domains of analysis. The high level of detail and granularity of this work is one of its added values and can provide a knowledge base for future researchers who intend to apply new methodologies for the analysis of investors’ decision-making. Starting from the results obtained so far, a new agenda for future research is suggested to encourage a more data-driven approach leveraging data science techniques.

Reviewing equity investors’ funding criteria: a comprehensive classification and research agenda

Ferrati F.
;
Muffatto M.
2021

Abstract

Venture capitalists and angel investors usually apply a set of assessment criteria to evaluate the key elements of entrepreneurial projects. However, since each investor considers different criteria, previous researchers who analysed investors’ decision making, ended up analysing a variety of divergent aspects. In this paper, a systematic literature review on the assessment criteria applied by equity investors was carried out. The purpose of this study was to identify and classify all the criteria considered by previous researchers to determine whether some aspects were investigated more extensively than others and to understand the reasons for this type of approach. After screening the abstracts of 894 journal publications, 53 articles were selected for a detailed analysis. In total, 208 unique criteria were identified and were subsequently classified into 35 specific categories, 11 generic classes and 4 main domains of analysis. The high level of detail and granularity of this work is one of its added values and can provide a knowledge base for future researchers who intend to apply new methodologies for the analysis of investors’ decision-making. Starting from the results obtained so far, a new agenda for future research is suggested to encourage a more data-driven approach leveraging data science techniques.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3393561
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