Factors Affecting the Affective Identity-Motivation to Lead (AI-MTL) of Lecturers: Case Study in X Unversity
Bibliographic record
Abstract
This study aims to explore the effect of Leadership Self-Efficacy (LSE), Past Leadership Experience (PLE),Organizational Identification (OI), and Perceived Job Stress as an Academic Leader (PJSAL) on AffectiveIdentity-Motivation to Lead (AI-MTL) of lecturers at the X University simultaneously. This study also aims toexplore the role of LSE in mediating relationship between PLE and AI-MTL as well as between PJSAL and AI-MTL. A total of 125 X University lecturers participated in this study (male: 53, female: 72; age range between26-71 years old), with data collected through an online questionnaire. Data analysis then was performed using theHierarchical Multiple Regression and Mediation Analysis. The result shows that there is a simultaneous effect ofLSE, PLE, OI, and PJSAL, in predicting AI-MTL of lecturers at the X University, F(4, 120) = 63.520, p < .001.All variables can explain 67.9% of the AI-MTL variation, R2 = .679. Meanwhile, PJSAL does not provide anymeaningful contribution to the AI-MTL variation. In addition, this study also confirms the role of LSE inmediating the relationship between PLE and AI-MTL partially, c’ = 1.0508, p < .001, and fully mediating therelationship between PJSAL and AI-MTL, c’ = -.006, p > .05. These results emphasize the strong need to identifytalents by using those factors, especially when universities have difficulty in finding their prospective 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".