Using interpretive structural modeling in venture capitalists’ decision-making process

Abstract

In today’s era, technology and innovative ideas had driven the global economy. A selection of venture investment process is regarded as the most complex task due to its direct or indirect impact on venture’s success rate. Venture capitalists (VCs) use a multi-criteria approach to identify new and incipient entrepreneurs while investing a new venture. At the times of investment, VCs assess several investment proposals based on some key factors such as entrepreneur’s personality, product and market characteristics, financial consideration, management skills and so on. Earlier researchers were concentrated on these factors solely; thus, a holistic approach is required for better understanding of venture capital investment process. To do so, interpretive structural modeling was used to explore and develop an inter-relationship among relevant decision-making factors. Structural self-interaction matrix was used to measure the level partitions at various iteration levels. A digraph facilitates the accomplishment of logical framework based on identified dependent (those affected by drivers) and driver (those affect others) factors. This paper may help VCs and entrepreneurs to better analyze the investment process and provide them a deep understanding of various issues associated with investment decision-making process.