Influence of Project Team Knowledge Diversity on Implementation of Building Construction Projects. A Case of Kajiado County, Kenya, Africa
Bibliographic record
Abstract
Implementation of the building construction projects is done by workers with diverse knowledge. The knowledge diversity can either be beneficial or detrimental to the implementation process. Out of the 50 building construction projects implemented by Kajiado county government in the financial year 2016/2017, 24 were not successfully implemented. This study sought to determine the influence of project team knowledge diversity on the implementation of building construction projects. The study used a pragmatism paradigm as well as a correlational research design and a sample of 251 respondents. Data was collected using semi-structured questionnaires, interview guides, and observation. Quantitative data was analysed for means and standard deviation as well as inferential techniques for correlation and regression while hypothesis was tested using ANOVA. Qualitative data was thematically analysed and the results triangulated with the quantitative results for presentation. The results indicated the existence of a positive correlation (r=0.323) between project team knowledge diversity and implementation of building construction projects. It was also established that 10.4% of the variations in implementation of the building construction projects are attributable to project team knowledge diversity R2=0.104 . The null hypothesis project team knowledge diversity has no significant influence on implementation of building construction projects was rejected based on F1,219=25.522, p=0.0000<0.05. It was concluded that project team knowledge diversity has a significant influence on implementation of building construction projects. The study recommends that recruitment into project teams for implementation of building construction projects should consider people with diverse knowledge backgrounds since they complement each other’s competencies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".