The adoption of human resources analytics in construction projects in Jordan: antecedents and consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The construction industry is increasingly using analytics tools to enhance decision-making and streamline project execution. However, human resource analytics (HRA) adoption has been slow due to concerns about cost and complexity. Recent studies investigating HRA adoption rely on conceptual models and are in their early stages. To address this gap, this study takes an empirical approach by examining the antecedents and impacts of HRA adoption on project performance in the Jordanian construction industry. A deductive conceptual framework based on technology-organisation-environment (TOE) and resource-based view (RBV) theories is developed, and 198 individuals are surveyed. Using structural equation modelling (PLS-SEM), the study identifies eight factors that significantly impact HRA adoption and shows that adoption leads to significant project performance improvements. The study provides valuable insights into HRA adoption in the construction industry, with implications for human resource management, project performance, and the industry as a whole.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it