Neural Network Model to Support International Market Entry Decisions

Date

2004-02-01

Author

Dikmen Toker, İrem
Birgönül, Mustafa Talat

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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.

Subject Keywords

Strategy and Management, Industrial relations, Civil and Structural Engineering, Building and Construction

URI

https://hdl.handle.net/11511/42396

Journal

Journal Of Construction Engineering And Management-Asce

DOI

https://doi.org/10.1061/(asce)0733-9364(2004)130:1(59)

Collections

Department of Civil Engineering, Article

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Citation Formats

İ. Dikmen Toker and M. T. Birgönül, “Neural Network Model to Support International Market Entry Decisions,” Journal Of Construction Engineering And Management-Asce, pp. 59–66, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42396.