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Altayeb, S (1992) Efficacy of Drug Testing Programs Implemented by Contractors. Journal of Construction Engineering and Management, 118(04), 780–90.

Bubbers, G and Christian, J (1992) Hypertext and Claim Analysis. Journal of Construction Engineering and Management, 118(04), 716–30.

Hijazi, A M, AbouRizk, S M and Halpin, D W (1992) Modeling and Simulating Learning Development in Construction. Journal of Construction Engineering and Management, 118(04), 685–700.

Hinze, J and Wiegand, F (1992) Role of Designers in Construction Worker Safety. Journal of Construction Engineering and Management, 118(04), 677–84.

Jaselskis, E J and Russell, J S (1992) Risk Analysis Approach to Selection of Contractor Evaluation Method. Journal of Construction Engineering and Management, 118(04), 814–21.

Liska, R W and Snell, B (1992) Financial Incentive Programs for Average‐Size Construction Firm. Journal of Construction Engineering and Management, 118(04), 667–76.

Moselhi, O, Fazio, P and Hason, S (1992) Automation of Concrete Slab‐on‐Grade Construction. Journal of Construction Engineering and Management, 118(04), 731–48.

Russell, J S and Jaselskis, E J (1992) Predicting Construction Contractor Failure Prior to Contract Award. Journal of Construction Engineering and Management, 118(04), 791–811.

  • Type: Journal Article
  • Keywords: Owners; Contractors; Failures; Predictions; Models; Decision making;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(1992)118:4(791)
  • Abstract:
    This paper discusses items that can aid owners in predicting the chance of construction contractor failure prior to contract award and thus assist them in the evaluation process. A predictive contractor failure model has been developed and is discussed. The model predicts the probability of contractor failure at the project level; failure is defined as a significant breach of the contractor's legal reponsibilities to the owner (for example, bankruptcy or material breach of contract related to meeting desired project objectives such as cost, schedule, and quality). Data for the model development were collected using two questionnare surveys. The modeling method involves the use of discrete choice logistic regression. Results show that four variables are strong predictors of contractor failure: (1) The amount of owner‐contractor evaluation; (2) whether cost monitoring was performed by the owner; (3) the level of support received by the project manager from the contractor's senior management throughout the course of the project; and (4) the early involvement of the contractor's project manager. The model was validated using an additional 36 projects.

Sanvido, V E and Paulson, B C (1992) Site‐Level Construction Information System. Journal of Construction Engineering and Management, 118(04), 701–15.

Syal, M G, Grobler, F, Willenbrock, J H and Parfitt, M K (1992) Construction Project Planning Process Model for Small‐Medium Builders. Journal of Construction Engineering and Management, 118(04), 651–66.

Thomas, H R, Sanders, S R and Bilal, S (1992) Comparison of Labor Productivity. Journal of Construction Engineering and Management, 118(04), 635–50.

Thomas, H R, Smith, G R and Ponderlick, R M (1992) Resolving Contract Disputes Based on Differing‐Site‐Condition Clause. Journal of Construction Engineering and Management, 118(04), 767–79.

Tommelein, I D, Levitt, R E and Hayes‐Roth, B (1992) SightPlan Model for Site Layout. Journal of Construction Engineering and Management, 118(04), 749–66.