Evaluation of Risk Management Practice in the Nigeria Construction Industry
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".