Major causes of problems between contractors and subcontractors in the Gaza Strip
Bibliographic record
Abstract
Purpose Subcontractors play a significant role in the Palestinian construction industry as about 90 per cent of the work is performed by subcontractors. The main objective of this paper is to identify and analyse the major problems which exist between the contractors and subcontractors in the construction industry in the Gaza Strip. Design/methodology/approach A questionnaire was designed to elicit contractors' and subcontractors' viewpoints regarding the most important causes of problems that affect the relationship between them. A total of 53 problems were identified based on a literature review, a pilot study was considered in this study and was listed under five groups. A total of 150 questionnaires were randomly sent to contractors and subcontractors. The respondents had extensive experience in the construction industry with average working experience of 20 years. The questionnaire was validated by the criterion‐related reliability test that measures the correlation coefficients between the factors selected for in each group and for all groups as one entity, and structure validity test (Spearman test). Findings The result of the survey indicated that, assigning part of the works to new subcontractor without informing the original subcontractor, contractor's financial problems, delay in contract progress payments, non‐adherence to the conditions of the contract, non‐adherence of the subcontractor to the time schedule, and lack of construction quality work were the most important causes of interface problems, while involvement in several projects with the contractor at the same time, weather conditions, and geological problems on site were considered as trivial causes of potential interface problems. Spearman's rank correlation tests showed that there are no differences in the viewpoints between contractors and subcontractors. Originality/value The research findings might assist practitioners to focus on major problems which have existed between the contractors and subcontractors in the construction industry in their present and future projects. By eliminating or minimizing these problems, subcontractors are encouraged to contribute significantly to the capital risk, resources, managerial effort, and business expertise supporting the largest industry in Palestine. The paper would be valuable for all academics and industry professionals involved in construction business in general.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".