A Two-Stage Stochastic Programming Model for Technological Knowledge Acquisition Based on Game Theory

Abstract

Firms outperforming competitors often get their success through innovation and new technological knowledge acquisition. This study offers a Three-Stage decision-making model for acquiring new technological knowledge and the optimal time to invest. In the first Stage, two competing firms decide to invest in a new technological knowledge without knowing its level. In the next stage, firms will develop and integrate it with their knowledge. Due to the uncertainty of new technological knowledge, a stochastic programming model is used to determine the optimal acquisition time. This model identifies the leader and follower by considering advantages such as branding and high market share as well as disadvantages such as high cost of uncertainty. Finally, we used Cournot and Stackelberg game to determine the winner in the market. The proposed model can be used as a decision-making tool to help organizations, in uncertainty, invest as leaders in acquiring new technological knowledge and entering the market, or wait until things are clear. The results of stochastic programing and game theory model show that the level of knowledge of firms at the time of production, knowledge absorption coefficient, and constant demand coefficient will have a special effect on determining the winner in the market.