GLOBAL COMPETENCIES: HOW MBA SCHOOLS DEVELOP CULTURALLY INTELLIGENT LEADERS
Bibliographic record
Abstract
Higher education institutions have internationalized for various reasons, one of which is to prepare students to be interculturally competent and to compete in a global labour market (Knight, 2004). The ideal global graduate is one who has the skill set to respond to the changing needs of the workplace, to have tolerance and respect for other cultures, and to have intercultural competence as a major skill for employers (Lilley, Barker, Harris, 2017; Sample, 2012; Spitzberg & Changnon, 2009). Business, trade, and investment have moved from the local to national and now to the global realm. Business education has seen rapid growth and expansion in the last few decades with the proliferation of MBA schools, however, business education curriculum has been criticized for not keeping pace with the expectations and changing needs of society and employers and is seen as deficient (McMillan & Overall, 2016). A growing gap exists between the traditional MBA curriculum and the competencies students need to work, and MBA schools have been criticized for not developing global competencies in students (Kedia, 2011, Aggarwal, 2011). This mixed methods research study measured the cultural intelligence (CQ) (Earley & Ang, 2003) of MBA students in their first year of a two-year program using the Cultural Intelligence Scale (CQS) survey and explored the perception of CQ using qualitative interviews. Deardorff’s Process Model of Intercultural Competence (PMIC) (2006) was incorporated as a conceptual framework in the exploration of cultural intelligence development. Data from the survey indicated an increase in the mean values for all four components of CQ: cognition, metacognition, motivation, and behaviour. Synthesizing the qualitative interview data provided students’ perception of cultural intelligence and highlighted its importance to their career and employment opportunities, as well as to their personal growth and development. Factors that contributed to CQ development focused on structured experiences of academic teams, group work, and unstructured experiences through socialization, conflict, and miscommunication. The results of the study contribute to discussions on the curriculum in relation to larger institutional internationalization efforts and highlight the importance of cultural intelligence as both a vital component of business education and the development of future global leaders.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".