Factors Affecting Crowdfunding Investor Number in Agricultural Projects: The Dummy Regression Model

Abstract

As an important sector in the Indonesian economy, the low capital problems in agriculture should be solved. Crowdfunding is one of the solutions gaining modal from society. This agricultural investment could be an attractive personal asset, too. But, no information yet about the complete characteristics of agricultural projects that fetching the people's participation to invest. Hence, the aim of this research is to find out the factors that influence people's participation in agricultural business investment with crowdfunding schemes. The quantitative data collected from 468 agricultural projects held by agricultural business startup PT. XYZ was analyzed. The six characteristics of the project are role as independents factors. They were project types (loans and profit sharing), project fields (agriculture, livestock, and fisheries), project risk (low, moderate, and high), project duration, the amount of capital needed and profit assumption. All the factors have been analyzed by a multiple regression model to estimate the number of investors. The results showed that the number of crowdfunding agriculture-based projects investor can be modeled by dummy regression. The longer project duration will decrease the number of investors by 0.577 people. The higher risk of the project will lower the number of investors. Based on the projects field, the most interesting projects are the livestock project, followed by agriculture and the last is the fisheries project. Overall, the project period, project risk and project field are the factors that influence people's participation in agricultural business investment with crowdfunding schemes. In the future, crowdfunding livestock projects with a short implementation period (around 3 months), and low risk can be sought so that more people participate in becoming investors. Keywords: crowdfunding, agriculture project, agricultural investment, multiple regression model, dummy regression