Level of Financial Performance of Selected Construction Companies in South Africa
Bibliographic record
Abstract
Purpose—There is no consensus on the indicators that assess a construction company’s financial performance projects undertaken. There is also a dearth of concepts on the financial performance indicators for construction companies in South Africa and indeed, the wider continent of Africa. This paper proposes novel financial performance indicators for assessing construction organizations and tests these on selected construction companies in the South African construction industry. Design/methodology/approach—This research employed a pragmatic approach. Contractors with financial credibility and capacity of ≥R 40 million, annual turnover of ≥R 20 million, and available capital of ≥R 40 million were purposively selected for this study. Parameters such as total revenue, direct cost of work, total indirect cost and total income were elicited from the sample contractors to assess their financial performance. The assessment was undertaken using formulas that were formulated based on the descriptions provided under the research methodology. Further analysis was conducted using post hoc Tukey’s honest significant difference (HSD). Findings—The study finds that construction companies with a strong structure, multiple areas of specialization, creative and efficient staff members, and access to funding, have a greater chance of experiencing higher: income; positive leverage; positive liquidity; and positive cash flow. Moreover, companies with specialization in civil engineering construction and project management skills experienced higher positive liquidity and profitability. Originality/value—This research is unique through its investigation and formulation of indicators for assessing the financial performance of construction companies. This research is consequently representing the first attempt to analyze financial data using the approaches prescribed and adopted.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".