Selection of a business school dean: a multi-criteria knowledge-based approach
Bibliographic record
Abstract
Purpose Business schools play a significant role in providing individuals with the ability to adapt to constantly changing environments. Such agile organizations require deans who, as leaders, possess the knowledge and attributes of astute and responsible executives. In this regard, the measurement of the attributes of leadership paves the way for evaluating a leader’s options process. In this study, we measure the attributes of leadership to pave the way for evaluating a leader’s decision-making process. Design/methodology/approach The rich data included the opinions of 93 university professors from seven countries: Iran, India, China, France, the UK, Canada and the USA. In appraising the responses, the authors considered the nationality and the development level of each participant’s country and continent. In this study, the authors developed an online questionnaire based on the best-worst method (BWM). By performing a one-way analysis of variance (ANOVA), the authors also determined the significant statistical differences of the scientific communities through the lenses of authentic leadership, leader-member exchange and social identity and leadership. Findings The results provide evidence of transparency, measured as the most important criterion for leading a business school, i.e. knowledgeable deanship. Furthermore, the findings reveal a meaningful difference between developed and developing countries in the context of an authentic leadership pillar. Originality/value This paper contributed to the literature in five major ways as follows: The authors investigated the attitudes of scientific communities from different countries, business schools, BWM, dean selection and leadership evaluation.By means of the BWM, the authors measured the criteria culminating in the selection of a knowledgeable leader for a business school.The authors compared and contrasted the attitudes of scientific communities in developing countries vis-à-vis those in developed ones.The authors addressed the differences and similarities among countries in relation to the selection of a knowledgeable business school leader.The authors provided beneficial insights by addressing the different perspectives of researchers on the weights of the criteria involved in the selection procedure for a business school dean.
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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 | 0.051 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".