Productivity management in the South African civil construction industry - factors affecting construction productivity
Bibliographic record
Abstract
Labour productivity in South Africa is at one of its lowest levels. During 2014 the civil construction industry contributed only 3.5% to the GDP of South Africa. It is faced with challenges such as an industry environment that is increasingly competitive, and organisations in the civil industry that experience financial difficulties, such as low profit margins. An industry-specific survey, using a questionnaire, was conducted to ascertain the perceptions of industry professionals regarding factors which have an impact on productivity. A literature study was done to identify the factors that have an impact on construction productivity, based on a global perspective. From the literature study, 12 studies were identified, and a benchmark was set with which to compare the findings of the research questionnaire. To obtain the relevant information through the questionnaire, a selective sampling process was used, as the focus of the research required a specific group of individuals who were involved in the management of projects in the civil construction industry. Two civil engineering organisations, the South African Forum of Civil Engineering Contractors and the South African Institution of Civil Engineering, were contacted to assist with the distribution of the questionnaire. The questionnaire consisted of 51 factors which the industry professionals had to rate, based on their experience. These factors had to be rated with the use of a 0-4 Likert scale, based on two specific questions: (1) What impact does the factor have on construction productivity? (2) What is the frequency of occurrence of the factor? A total of 40 questionnaires were completed by the industry professionals. Thereafter the ranking of the factors was calculated with the use of the relative importance index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".