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A neural network bid/no bid model: the case for contractors in Syria

Author

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  • Mohammed Wanous
  • Halim Boussabaine
  • John Lewis

Abstract

Despite the crucial importance of the 'bid/no bid' decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back-propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the 'bid/no bid' decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real-life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision-making process. The model offers a simple and easy-to-use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision-making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.

Suggested Citation

  • Mohammed Wanous & Halim Boussabaine & John Lewis, 2003. "A neural network bid/no bid model: the case for contractors in Syria," Construction Management and Economics, Taylor & Francis Journals, vol. 21(7), pages 737-744.
    • Handle: RePEc:taf:conmgt:v:21:y:2003:i:7:p:737-744
    DOI: 10.1080/0144619032000093323
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    Citations

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    Cited by:

    1. Yu-Shan Chen & Ke-Chiun Chang, 2010. "Using the fuzzy associative memory (FAM) computation to explore the R&D project performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(3), pages 537-549, April.
    2. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    3. Qiao, Yu & Labi, Samuel & Fricker, Jon D., 2021. "Does highway project bundling policy affect bidding competition? Insights from a mixed ordinal logistic model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 228-242.
    4. Fuchigami, Helio Yochihiro & Tuni, Andrea & Barbosa, Luísa Queiroz & Severino, Maico Roris & Rentizelas, Athanasios, 2021. "Supporting Brazilian smallholder farmers decision making in supplying institutional markets," European Journal of Operational Research, Elsevier, vol. 295(1), pages 321-335.
    5. Yu-Shan Chen & Ke-Chiun Chang, 2013. "The nonlinear effect of green innovation on the corporate competitive advantage," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 271-286, January.
    6. Zhang, Sifei & Yuan, Chien-Chung & Chang, Ke-Chiun & Ken, Yun, 2012. "Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network," Journal of Informetrics, Elsevier, vol. 6(4), pages 485-495.
    7. Yu-Shan Chen & Yu-Hsien Lin & Tai-Hsi Wu & Shu-Tzu Hung & Pei-Ju Lucy Ting & Chen-Han Hsieh, 2019. "Re-examine the determinants of market value from the perspectives of patent analysis and patent litigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 1-17, July.

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