The Effect of Signals on New Venture Funding in the Context of an Emerging Market

Abstract

New ventures need to access external financial resources to exploit market opportunities. As they do not yet have track records such as proven technologies, finished products, and verified market demand, prospective investors need to rely on signals of quality such as founding team characteristics or third-party endorsements. Most of the existing knowledge on new venture funding builds on insights from research conducted in the context of developed economies. Emerging economies are characterized by institutional voids in product, labor, and capital markets, as well as a weak regulatory system and contract-enforcing mechanisms. New ventures may require some context-relevant capabilities to survive and prosper in this environment. Relatedly, investors’ choices of new ventures to fund will likely be shaped by signals of these context-relevant capabilities alongside universal signals that reflect the viability of a new venture and the likelihood that it will advance. In this thesis study, I investigate independent and interdependent effects of these signals on investors’ choices of startups to fund. I take a configurational approach to examine complex interdependencies between signals, applying the fuzzy-set QCA method. The Turkish startup ecosystem constitutes the empirical setting of my study. I examine early-stage funding in high-tech industries where signals are most relevant due to high levels of uncertainty. Findings have important theoretical implications for the literature on new venture funding and signaling processes, as well as some practical implications for new ventures in emerging market environments.