A PREFERRED LEADERSHIP PORTRAIT OF SUCCESSFUL CROSS-CULTURAL LEADERSHIP
Bibliographic record
Abstract
Purpose: This article seeks to highlight the significance of the understanding of the cultural dimensions, global leadership attributes, and leadership profiles of the home culture of Malaysia in comparison to the adopted host culture of Canada to incorporate the best cross-cultural leadership practices. It presents the preferred leadership portrait of successful cross-cultural leadership. Findings: Cross-cultural competence has become a considerable important research in the last two decades. Cross-cultural studies on cross-cultural differences in leadership interaction of the home and host cultures of leaders are needed. It is appropriate to consider the preferred leadership portrait that adapts to the cultural dimensions, global leadership attributes, and leadership profiles of the leader’s home and host cultures for effective cross-cultural leadership practice. Research limitations/implications: The findings of this conceptual review paper need further study to validate the application of the adaptation of the cultural dimensions, global leadership attributes, and leadership profiles of the related home and host countries based on the GLOBE study. Practical implications: There are values in the understanding of the application of cross-cultural principles based on cross-cultural research information for cross-cultural leadership adaptation and practices. The knowledge of the related leadership, cultural factors will facilitate cross-cultural understanding and interrelation. Leaders today are to develop the competencies to be effective in the globally connected societies as well. Originality/value: This paper on cross-cultural leadership used findings based on the GLOBE studies as the main text to understand the various cultural factors that have an impact on leadership. The information on the cultural dimensions, global leadership attributes, and leadership profiles of the home and adopted host countries were compared and contrasted to construct the best approach for cross-cultural leadership practices. The concept of the preferred leadership portrait is in congruent to the leadership, sustainability concept that promotes the long term view and progress of leaders, systems, and organizations. JEL Classification: M16
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".