Neural network model to support international market entry decisions

Bidding for international construction projects is a critical decision for companies that aim to position themselves in the global construction market. Determination of attractive projects and markets where the competitive advantage of a company is high requires extensive environmental scanning, forecasting, and learning from the experience of competitors in international markets. In this paper, a neuronet model has been developed as a decision support tool that can classify international projects with respect to attractiveness and competitiveness based on the experiences of Turkish contractors in overseas markets. The model can be used to guide decision makers on which type of data should be collected during international business development and further help them to prepare priority lists during strategic planning. Information derived from the model demonstrates that the most important factors that increase attractiveness of an international project are availability of funds, market volume, economic prosperity, contract type, and country risk rating. Similarly, level of competition, attitude of host government, existence of strict quality requirements, country risk rating, and cultural/religious similarities are the most important factors that affect competitiveness of Turkish contractors in international markets.