Challenges negating virtual construction project team performance in the Middle East
Bibliographic record
Abstract
Purpose Over the last couple of decades, many organisations are increasingly adopting virtual team concepts, and construction companies in the Middle East are no exception. Members of a virtual team are geographically scattered and represent a diverse range of cultures. Thus, challenging issues emerge more frequently than in a traditional team. There are challenges associated with space and time as well as high client's demand. Therefore, this study aims to identify and probe the causes of the challenges in virtual project teams in the construction industry of the Middle East. Design/methodology/approach A list of challenges was derived through a comprehensive review of relevant literature. Questionnaire survey was conducted with professionals who are involved in construction virtual project teams. Further, the factor analysis technique was used to analyse the survey responses. Findings The results show that the challenges in virtual team arrangement in the Middle East construction industry can be grouped into seven categories, namely, organisational culture, conflict within the team, characteristics of the team members, trust within the team members diversity of the team, communication and training, and cohesion in the team. Understanding of these factors will drive the needed platform to support effective virtual project teams in the Middle East. Originality/value This study raises the prospect that organisations may establish an environment for team members to achieve higher levels of virtual cooperation by concentrating on these potentially crucial factors. This, in turn, will encourage further innovation and performance within construction organisations.
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 | 0.007 | 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 | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".