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Title: A methodology for predicting company failure in the construction industry
Author: Abidali, Adnan Fadhil
ISNI:       0000 0001 3391 0834
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 1990
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This thesis develops the theory of failure prediction for UK oprotruction companies. A questionnaire was devised and included 17 questions related to failure for both an "at risk" group classified as vulnerable, i.e. those scoring negatively by the Z-score model and a positively scoring "solvent" group. The existence of managerial factors related to failure was investigated in the questionnaire using a multiple choice method. Both groups proved adequate for ccmparison purposes, and were therefore included in the A-score model. The A-score for a company is obtained by adding the weight of all factors and errors together, and a cut-off value determined. The model was statistically verified by the t-test method at 1% significant level and further examined by the Willcoxon (Rank Sum) tests null hypothesis rejected at 5% level of significance. An attempt was also made to relate A-score and Z-score values, unfortunately statistical analysis indicated only 67.7% intercorrelation between A-score and Z-score i.e. not very strong. However, the Z-score value of zero corresponded to an A-score cut-off value of about 50, these being critical values in both modes. Finally, trend analysis was shown to be a suitable extra check in objective evaluation of company performance, and an improved method of systematically appraising contractors was produced. However, the developed models should only be used as part of an overall asse4mment of company stability. Any predictions should be interpreted with caution as the models require further testing on a broader range of companies. It is also important to appreciate that the use of such models to exclude ocmpanies from tender lists could accelerate or even cause failure.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Management & business studies Management Economics Mathematical statistics Operations research