An Analysis of the Relationship between Value Diversity and Project Implementation. A Case of Building Construction Projects in Kajiado County, Kenya, Africa.
Bibliographic record
Abstract
The process of implementing building construction projects usually involves teams that are made up of individuals who are diverse in several aspects, which can be classified as either deep-level or surface-level diversity. Diversity in work-values has been seen to influence project performance in different fields. During the financial year 2016/2017, the County Government of Kajiado implemented a total of 50 building construction projects, which were meant to be completed within the financial year, but only 48% of the projects were deemed to be successfully implemented by the end of the period. Several factors have been attributed to the low success rate but none has looked at diversity in values among project team member. This research sought to determine the strength and nature of relationship between project team value diversity and building construction projects implementation process in Kajiado county. A pragmatic paradigm was employed in the research. Data was collected using semi-structure questionnaires from a random sample of 251 respondents while interviews were done on 11 key respondents. Descriptive statistics such as percentages, means and standard deviations as well as inferential statistics like correlation and regression were generated from the quantitative data while qualitative data was thematically analysed. It was established that a moderate positive correlation (r=0.401 ) existed between value diversity and project implementation process. It can therefore be inferred that value diversity positively influences the process of implementation of building construction projects.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".