Successful Joint Venture Strategies Based on Data Mining
This paper is to propose types of joint ventures that can increase the competitiveness of a company in the marketplace. The authors examine the characteristics of individual venture enterprises based on technology. They considered sixteen Technology Evaluation Attributes (TEA) in order to categorize companies into four groups. Next, they used a multinomial logistic regression model to identify the significant characteristics of a venture company that successfully predicts group membership. Based on this information, they propose various forms of joint venture which complement each other and produce higher overall competence. The results of this study can provide important feedback information to academics, policy-makers, and venture capitalists.