Virtual Team Dynamics: Fostering Intercultural Absorption Capacity in a Global Context
Bibliographic record
Abstract
In a technologically interconnected world, managers face the pressing need to train individuals for effective collaboration within global virtual teams (GVTs). Despite the importance of fostering knowledge acquisition for top-tier management in these diverse settings, there is a notable gap in understanding the best pedagogical approaches. This study highlights the significance of Intercultural Absorptive Capacity (iACAP) in fostering successful collaboration in newly established GVTs conducted in a matched real-world consulting project in an educational setting. In our study, we refine and validate an instrument to assess iACAP within virtual team settings. We explore the shifts in iACAP before and after projects in global virtual teams. We identify two key factors that influence iACAP's advancement in educational environments. The role of peer feedback as a mediating self-improvement mechanism and the course/training context, focusing on motivation, clarity, and team collaboration enjoyment in predicting the ultimate iACAP. Using covariance-based structural equation modelling, we validate this tool and test our hypotheses with data from 345 participants within GVTs. The research contributions are twofold: a) the introduction of a tailored tool to gauge adaptive intercultural absorptive capacity in global virtual teams, and b) insights into team management based on a comprehensive multinational analysis.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".