Cultural intelligence and mindfulness: teaching MBAs in Iran
Bibliographic record
Abstract
Purpose A dynamic global economy has increased the need for cross-cultural flexibility and cultural intelligence (CQ). While a large literature has examined various means to increase CQ in student and expatriate populations, its importance for teachers in cross-cultural settings has been largely unexamined. This paper aims to use the experiences of a group of professors in an MBA programme in Iran to investigate the effect of their activity on their cross-cultural skills. Design/methodology/approach Using structured interviews and content analysis, the authors draw on the experiences of business faculty from a Canadian business school who helped deliver an MBA programme in Iran to investigate how their experiences in a country new to them were reflected in the components of CQ. Findings Using an established model of CQ, the authors find contributions to all three facets, knowledge, mindfulness and behaviour, indicating that such exchanges can be regarded as important for students and teachers alike in an international educational context. Originality/value With more and more teaching extending across cultural boundaries in both domestic and international settings, the capacity of instructors to read, interpret and react to the attitudes, beliefs and behaviours of their students is an important factor in the success of these programs. To this point, at least within the business education literature, the influence of such encounters on the instructors involved has been neglected.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".