Cultural intelligence and cooperation in the construction industry: the mediating role of trust
Bibliographic record
Abstract
Purpose This study aims to investigate the mediating role of trust in the relationship between cultural intelligence (CQ) and cooperation among construction professionals. Furthermore, this study assesses perceived differences in CQ, trust and cooperation between individuals with and without experience working with foreigners in the construction industry. Design/methodology/approach Data were gathered from a cross-sectional survey of 408 engineers in Myanmar’s construction industry. A confirmatory factor analysis validated structural equation modeling approach was used to address research hypotheses, and an independent samples t-test was performed to identify the perceived differences between two categories of respondents. Findings The structural equation modeling results identified CQ as a positive direct predictor of cooperation, affect-based trust and cognition-based trust. Both affect-based trust and cognition-based trust directly and positively influenced cooperation. The relationship between CQ and cooperation was partially mediated by affect-based trust and cognition-based trust. The findings of the independent samples t-test revealed that construction employees with prior experience working with foreigners tend to exhibit a higher level of CQ, trust and cooperation than their counterparts. Originality/value The present study added the mediating role of trust in CQ and cooperation linkage, an area that has received limited attention in the literature.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".