J. Nicholas
University of Wolverhampton
9 Papers
67 Citations
J. Nicholas is an academic researcher from University of Wolverhampton. The author has contributed to research in topics: Decision support system & Debt. The author has an hindex of 5, co-authored 9 publications.
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Papers
Using multivariate techniques for developing contractor classification models
TL;DR: In this article, the authors investigated the intrinsic link between clients' selection preferences and contractors' performance using logistic regression and multivariate discriminant analysis (MDA) techniques and found that suitability of the equipment, past performance in cost and time on similar projects, contractor relationship with local authority, and contractor reputation/image were the most predominant project specific criteria (PSC) in the LR and MDA models among the 34 PSC.
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Contractor financial credit limits; their derivation and implications for materials suppliers
TL;DR: In this article, a conceptually new approach is presented to identify whether an additional contractor's trade results in a worthwhile gain in utility for the supplier, and it is identified that allowing very few contractors credit facilities that account for a large proportion of suppliers' potential profits, having inaccurate creditworthiness evaluation procedures, and operating on low targeted profit margins are the characteristics that inflict maximum financial risk upon materials suppliers.
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Suppliers' debt collection and contractor creditworthiness evaluation
TL;DR: In this article, the results of a survey of UK construction materials suppliers' credit control and debt collection procedures are presented, highlighting the need for future research into contractor creditworthiness evaluation.
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•Book Chapter
Distance learning and the empowerment of students: applied statistical analysis for students of the Built Environment.
J. Nicholas,David J. Edwards +1 more
- 01 Jan 2002
TL;DR: This article found that over 50% of students in the School of Engineering and the Built Environment (SEBE) attend University on a part-time basis, and this problem is further exacerbated by the reference to many introductory statistical texts that are written for persons who have an "above average" mathematical knowledge.
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