Early-stage start-up selection is a critical yet challenging task for venture capital (VC) investors due to high uncertainty, limited historical data, and rapidly evolving business environments. Traditional evaluation processes often fall short in systematically handling multiple qualitative and uncertain factors that influence start-up success. As a result, there is a growing demand for robust decision models that can support VC firms in identifying promising early-stage ventures more accurately and consistently. This study presents a hybrid fuzzy multi-criteria decision-making approach tailored to the needs of venture capital investment under uncertainty. The model integrates expert judgment using the proportional spherical fuzzy AHP method to evaluate the relative importance of key dimensions. Then, spherical fuzzy TOPSIS is applied to rank investment alternatives based on their overall performance rankings. The proposed framework enables VC decision-makers to incorporate both subjective insights and data ambiguity in a structured and transparent way. It offers a practical tool to enhance the reliability of early-stage investment evaluations and improve the effectiveness of venture capital portfolio strategies.
Kellekci et al. (Mon,) studied this question.