Funding Innovation and Risk: A Grey-Based Startup Investment Decision

Abstract

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India’s booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors’ early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.