Abstract
This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, AmazonElastic ComputeCloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.
Highlights
The construction industry remains a major player of any country’s economy
This study aimed to propose a framework architecture for developing a big data analytics (BDA) construction business failure prediction model (CB-failure prediction models (FPM)) and implementing it, using data of tens of thousands of construction firms
It was discovered that MapReduce, which is the traditional big data analyser, is not fit to develop BDA CB-FPM because of its poor support for iteration
Summary
The significance of any country’s (or region’s) economy is such that the sustainable development of the country largely hinges upon it [1]. The absolute significance of a country’s economy cannot be overemphasized anything that contributes to, or affects, it significantly is usually of national/global concern. The department went further to explain that “construction has a much wider significance to the economy. It creates, builds and maintains the workplaces in which businesses operate and flourish, the economic infrastructure which keeps the nation connected, the homes in which people live and the schools and hospitals which provide the crucial services that society needs. According to Rhodes [4] in a House of Commons Library research paper, the CI in 2014 contributed £103 billion in economic output, representing 6.5% of the total; it provided 2.1 million jobs or 6.2% of the UK total in 2015
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