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The effect of contract type and size on competitiveness in bidding

Author

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  • Derek Drew
  • Martin Skitmore

Abstract

Multiple regression is used to construct a prediction equation relating bidder competitiveness (the dependent variable) to the independent variables of bidder, contract type and contract size. The regression model shows that differences in contractor competitiveness are greater for different contract sizes than for different contract types. The most competitive contractors appear to be those with a preferred contract size range. Such a model can be used as part of a more systematic approach in prequalifying contractors. It may also be used by contractors as a basis for assessing bidding performance.

Suggested Citation

  • Derek Drew & Martin Skitmore, 1997. "The effect of contract type and size on competitiveness in bidding," Construction Management and Economics, Taylor & Francis Journals, vol. 15(5), pages 469-489.
    • Handle: RePEc:taf:conmgt:v:15:y:1997:i:5:p:469-489
    DOI: 10.1080/014461997372836
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    References listed on IDEAS

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    1. Patricia M. Hillebrandt, 1985. "Economic Theory and the Construction Industry," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-1-349-17934-3, March.
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    Cited by:

    1. Derek Drew & Po Yin Lai & Heng Li & H. P. Lo, 2002. "Correcting the fee-technical score variability imbalance in two-envelope fee tendering," Construction Management and Economics, Taylor & Francis Journals, vol. 20(2), pages 157-166.
    2. Mohammed Fadhil Dulaimi & Hon Guo Shan, 2002. "The factors influencing bid mark-up decisions of large- and medium-size contractors in Singapore," Construction Management and Economics, Taylor & Francis Journals, vol. 20(7), pages 601-610.
    3. Heinz Herrmann, 2019. "How to Increase Profits Through Predictive Analytics When Only Few Competitors’ Bids Are Known," FIIB Business Review, , vol. 8(1), pages 61-76, March.
    4. Joe Wong & Eddie Hui, 2006. "Construction project risks: further considerations for constructors' pricing in Hong Kong," Construction Management and Economics, Taylor & Francis Journals, vol. 24(4), pages 425-438.

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