Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah
In today's competitive business environment, an increasing number of businesses are experiencing economic and financial challenges, which may result in bankruptcy. As a result, there has been a rise in research conducted on the causative factors, effects, and forecasts of business insolvency an...
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2023
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my-uitm-ir.886392024-01-02T04:59:03Z Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah 2023 Abdullah, Nur Diyana In today's competitive business environment, an increasing number of businesses are experiencing economic and financial challenges, which may result in bankruptcy. As a result, there has been a rise in research conducted on the causative factors, effects, and forecasts of business insolvency and bankruptcy. Therefore, predicting business failure is one of the most basic concepts to consider when assessing solvency, especially in a turbulent economy. Due to the importance of construction companies to the country's economy, it is essential to predict business failure to avoid future failure or distress of these companies. Thus, this study's primary objective is to create a prediction model that may be used to analyse and detect the likelihood of financial distress and bankruptcy of a construction firm in some construction companies in Bursa, Malaysia. Furthermore, the study also determined the crucial elements that would enable early detection of the signs of impending financial failure of businesses in the construction sector. Due to the need for accurate financial distress prediction, the Statistical Package for Social Sciences (SPSS) and partial least squares (PLS) analysis were employed for data screening, assessment, and validation of the measurement and structural model. The data used in this study consisted of historical financial statements when the selected construction companies entered PN17 status before being listed by Bursa Malaysia. Software for model prediction was developed to analyse the failure and health status of the companies. The advantage of this software is obtaining failure status with the faster result. By incorporating construction businesses data from year 2015 to 2020 from Bursa Malaysia, it is possible to measure these businesses' failure and health status with an accuracy of 83% based on the proposed model. The proposed model's outcomes show excellent prediction with a moderate and substantial coefficient of the determinant (R2). The finding of this study establish relationship between financial ratios, macroeconomic indicators, and company conditions. Only profitability demonstrated a considerable ability to forecast the firm situation among many types of financial ratios studied. Profitability makes sense as the key factor in the prediction since a company may experience debt if it cannot make any profit. The results of this ratio show negative effects between issues related to debt levels and company's financial health several years before it fails. Therefore, the proposed model able to identify each variable's function in the prediction model and select the most appropriate financial measures to predict insolvency. Thus, this study findings of the business insolvency factors would help to identify a crucial aspect that the Malaysia National Construction Policy 2030 should consider to expedite technology adoption in all building work processes and align the sector with the nation's long-term decarbonization agenda. 2023 Thesis https://ir.uitm.edu.my/id/eprint/88639/ https://ir.uitm.edu.my/id/eprint/88639/1/88639.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Civil Engineering Abdul Malik, Sulaiman |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Abdul Malik, Sulaiman |
| description |
In today's competitive business environment, an increasing number of businesses are experiencing economic and financial challenges, which may result in bankruptcy. As a result, there has been a rise in research conducted on the causative factors, effects, and forecasts of business insolvency and bankruptcy. Therefore, predicting business failure is one of the most basic concepts to consider when assessing solvency, especially in a turbulent economy. Due to the importance of construction companies to the country's economy, it is essential to predict business failure to avoid future failure or distress of these companies. Thus, this study's primary objective is to create a prediction model that may be used to analyse and detect the likelihood of financial distress and bankruptcy of a construction firm in some construction companies in Bursa, Malaysia. Furthermore, the study also determined the crucial elements that would enable early detection of the signs of impending financial failure of businesses in the construction sector. Due to the need for accurate financial distress prediction, the Statistical Package for Social Sciences (SPSS) and partial least squares (PLS) analysis were employed for data screening, assessment, and validation of the measurement and structural model. The data used in this study consisted of historical financial statements when the selected construction companies entered PN17 status before being listed by Bursa Malaysia. Software for model prediction was developed to analyse the failure and health status of the companies. The advantage of this software is obtaining failure status with the faster result. By incorporating construction businesses data from year 2015 to 2020 from Bursa Malaysia, it is possible to measure these businesses' failure and health status with an accuracy of 83% based on the proposed model. The proposed model's outcomes show excellent prediction with a moderate and substantial coefficient of the determinant (R2). The finding of this study establish relationship between financial ratios, macroeconomic indicators, and company conditions. Only profitability demonstrated a considerable ability to forecast the firm situation among many types of financial ratios studied. Profitability makes sense as the key factor in the prediction since a company may experience debt if it cannot make any profit. The results of this ratio show negative effects between issues related to debt levels and company's financial health several years before it fails. Therefore, the proposed model able to identify each variable's function in the prediction model and select the most appropriate financial measures to predict insolvency. Thus, this study findings of the business insolvency factors would help to identify a crucial aspect that the Malaysia National Construction Policy 2030 should consider to expedite technology adoption in all building work processes and align the sector with the nation's long-term decarbonization agenda. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Abdullah, Nur Diyana |
| spellingShingle |
Abdullah, Nur Diyana Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| author_facet |
Abdullah, Nur Diyana |
| author_sort |
Abdullah, Nur Diyana |
| title |
Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| title_short |
Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| title_full |
Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| title_fullStr |
Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| title_full_unstemmed |
Failure prediction model in determining business insolvency of construction companies in Malaysia / Nur Diyana Abdullah |
| title_sort |
failure prediction model in determining business insolvency of construction companies in malaysia / nur diyana abdullah |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Civil Engineering |
| publishDate |
2023 |
| url |
https://ir.uitm.edu.my/id/eprint/88639/1/88639.pdf |
| _version_ |
1794192136578531328 |