Drivers for adopting augmented reality and virtual reality technologies in the construction project management in Gaza City
Bibliographic record
Abstract
This study explores the motivations and opportunities for adopting Augmented Reality (AR) and Virtual Reality (VR) technologies in construction project management in Gaza. A quantitative method was used, involving a questionnaire survey of 40 construction professionals. From an initial list of 35 potential drivers identified through a literature review, 33 were finalized after validation and pre-testing. These drivers were categorized into three groups: Improving Project Performance, Enhancing Company Image, and Boosting Overall Company Performance. Data analysis using SPSS revealed that the most influential drivers were real-scale design visualization, better understanding of design impacts, improved project comprehension, visualization of construction progress, and enhanced understanding of client requirements. In contrast, government incentives were ranked lowest in influence. The results highlight the significant potential of AR and VR to enhance design interpretation and project delivery in Gaza’s construction sector. The study recommends targeted strategies and training for construction practitioners to optimize the use of these technologies. By filling a research gap, the findings offer practical insights for professionals, policymakers, and researchers aiming to integrate AR and VR in construction, particularly in conflict-affected or resource-limited regions where such tools could substantially improve project outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".