Choosing Business Collaborators Using Computing Intelligence Methods

Abstract

Inter-firm collaboration has become a common feature of the developing international economy. Firms as well as the nations have more relationships with each other. Even relatively closed economies or industries are becoming more open, Australia and China are examples of this case. The benefits generated from collaboration and the motivations to form collaboration are investigated by some researchers. However, the widely studied relationships between collaboration and profits are based on tangible assets and turnovers whereas most intangible assets and benefits are neglected during the economic analysis. In the present paper, two methods, naive Bayes and neural network, from computing intelligence are used to study the benefits acquired from collaboration. These two methods are used to learn the relationship and make prediction for a specified collaboration. The proposed method has been applied to a practical case of WEMOSOFT, an independent development department under MOBOT. The predication accuracies are 87.18% and 92.31%, for neural network and naive Bayes, respectively. Experimental result demonstrates that the proposed method is an effective and efficient way to prediction the benefit of collaboration and choose the appropriate collaborator.