J. Nicholas
University of Wolverhampton
5 Papers
65 Citations
J. Nicholas is an academic researcher from University of Wolverhampton. The author has contributed to research in topics: Debt & Bad debt. The author has an hindex of 4, co-authored 5 publications.
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Papers
Towards standardising the assessment of flood damaged properties in the UK
TL;DR: In this paper, a conceptual model for assessing flood damage to UK domestic properties is presented, based on a critique of existing knowledge in the field and from discussions held with practitioners responsible for surveying and recommending strategies for repair of such properties.
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The state of health and safety in the UK construction industry with a focus on plant operators
David J. Edwards,J. Nicholas +1 more
TL;DR: In this article, the authors compared the accident rates occurring within the UK construction industry to those occurring within other industries, and found that the construction industry is arguably the most hazardous industry and has consistently recorded a poor accident record.
51
Forecasting UK construction plant sales
TL;DR: In this paper, an autoregressive moving average (ARMA) time series model is constructed using economic data relating to a 15-year period (1985-99) and a forecast of machine sales for year 2000 is made.
8
Impacts of credit control and debt collection procedures upon suppliers’ turnover
TL;DR: In this paper, the authors used stepwise multivariate discriminant analysis to identify that suppliers' turnover was related to whether they held insurance against bad debt, the total number of credit accounts furnished, the percentage of debting contractors who have credit limits imposed upon them, and whether guarantees of payment are sought.
8
Forecasting construction materials suppliers’ financial turnover
TL;DR: In this paper, it is shown that a supplier's financial turnover movement can be modelled and predicted with some accuracy by considering a number of characteristics of their credit control department, and the statistical technique of multivariate discriminant analysis (MDA) is used.
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