Evaluation of Risk Management Practice in the Nigeria Construction Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The construction industry is an essential contributor to a country’s economic growth. Unfortunately, the sector's contribution to the economy is hindered by numerous risk surrounding a construction project. Despite the harmful effect of construction risk, it cannot be eliminated but it can only be managed. Therefore, this study aimed at evaluating the practice adopted for managing construction risk within Nigeria construction industry. The quantitative research approach was adopted, and a descriptive study was selected because it gives an accurate account of the characteristics, for example, the behaviour, opinions, abilities, beliefs and knowledge of a situation or group. The questionnaire was sent out to 200 respondents out of which a total of 150 questionnaires were valid. All the valid questionnaires were analysed using SPSS v23 adopting the exploratory factor analysis method. The findings showed that just like developed countries the Nigeria construction industry adopt the best practice of risk management in construction projects. These practices include risk identification, assessment, response and control. The exploratory factor analysis revealed that under risk identification the practice adopted by the construction professionals is dived into information sourcing and history of the project. Concerning risk assessment, the practice comprises of event analysis and creating a picture of the project. The method adopted for risk response includes generating a risk reduction methodology, establishing risk management back up plan and shifting the risk to a third party. Whereas for risk control the practice consists of enhancing construction project quality and improving the program plan of the construction project. The study contributes to the better management of construction project risk in Nigeria.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it