Consequences of COVID-19 on Jordan’s construction sector using data mining techniques

Abstract

Globally, COVID-19 had devastating consequences on the construction sector, but there is a little knowledge of how the pandemic impacted developing nations. This study aims to investigate the job stability of employees in the construction sector and to highlight the factors that negatively affect employment. A survey of 1000 questionnaires was distributed to construction sector personnel and 436 valid responses were returned. Three popular data mining techniques were utilized: binary logistic regression, support vector machine, and Bayesian networks. Twelve models were developed; one binary logistic model, four support vector machine models, and six Bayesian networks models. The results of these models were compared based on accuracy, sensitivity, specificity, precision, recall, F-measure, and ROC Area. As a result, it was found that the method of the Bayesian networks was more effective in modelling the job stability of employees in comparison with the other models. According to our study, craft labourers were most affected in terms of job losses, followed by site engineers, while project managers and contractors were the least affected. The findings highlight the importance of protecting the most vulnerable labourers by revising the current legislation policy initiatives that should concentrate on establishing a better and more sustainable labour market.